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All Journal OPERATION EXCELLENCE: Journal of Applied Industrial Engineering Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Indonesian Journal of Halal Research Jurnal Optimalisasi Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Pengabdian kepada Masyarakat Nusantara Dinamika Kerajinan dan Batik : MAJALAH ILMIAH Brilliance: Research of Artificial Intelligence Jurnal Industri dan Inovasi (INVASI) Jurnal Polimesin Dinamika Kerajinan dan Batik: Majalah Ilmiah
Rizki Agam Syahputra
Universitas Teuku Umar
Religion Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Public Health Social Sciences Transportation Other

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

 1 
Sentiment Analysis on Tabungan Perumahan Rakyat (TAPERA) Program by using Support Vector Machine (SVM) Syahputra, Rizki Agam; Arifin, Riski; ., Suryadi; Iqbal, Muhammad
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8694

Abstract

This study aims to analyze public sentiment towards the Housing Savings Program (TAPERA) using the Support Vector Machine (SVM) algorithm. The dataset comprises 16,061 reviews about TAPERA which was gathered from web scrapping and YouTube API. The sentiment analysis results indicate that 99.8% of the reviews are negative, while only 0.2% are positive. The SVM model applied in this study achieved a very high accuracy rate of 99.81%. This indicates that the model is highly effective in classifying sentiments, particularly in identifying negative sentiments. The resulting confusion matrix shows the model's excellent performance in detecting negative sentiments, with no False Positives (FP) and a very high number of True Negatives (TN). However, the model exhibits weaknesses in detecting positive sentiments, as indicated by the presence of several False Negatives (FN) and the absence of True Positives (TP). The findings of this study suggest that the public generally holds a very negative view of the TAPERA program. This insight is crucial for program administrators to consider as they evaluate and improve the program based on negative feedback received from the public. Overall, this research provides important insights into public perceptions of TAPERA and underscores the need for better modeling for more representative sentiment analysis. These findings can serve as a basis for policymakers in designing more effective communication strategies and program improvements to increase public acceptance of TAPERA.
A Predictive Model for Crop Irrigation Schedulling Using Machine Learning and IoT-Generated Environmental Data Syahputra, Rizki Agam; Andriani, Dewi
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10193

Abstract

This study develops and evaluates a machine learning model for predicting optimal irrigation schedules using real-time environmental data collected from an Internet of Things (IoT) system. Building upon a previously validated smart farming monitoring system that provided real-time data on temperature, humidity, and soil moisture, this research addresses the next step: moving from monitoring to predictive analytics. Data collected over a six-day period from DHT11 temperature and humidity sensors, as well as soil moisture sensors, were used to train a predictive model. The model is designed to forecast future soil moisture levels, thereby providing farmers with proactive recommendations for irrigation. A Long Short-Term Memory (LSTM) neural network was employed to capture the temporal dependencies between atmospheric conditions and soil moisture. The model was trained on a portion of the collected data and then validated on a separate, unseen dataset. The evaluation yielded a Mean Absolute Error (MAE) of 2.5%, a Root Mean Square Error (RMSE) of 3.1%, and an R-squared (R2) value of 0.92, demonstrating high predictive accuracy. This approach aims to enhance water resource management, reduce manual intervention, and improve crop health by ensuring water is supplied only when necessary. The results indicate that the machine learning model can accurately predict irrigation needs, offering a significant improvement over traditional, reactive monitoring systems and marking a substantial step towards data-driven, precision agriculture.
Co-Authors Abdiel Khaleil Akmal Adib Adib Adib Adib, Adib Andriansyah Arifin, Riski Arrazy Elba Ridha Aulia Putri, Cut Widy Baehaqi Chairil Akbar Cleopatra, Ratu Cut Widy Aulia Putri Dewi Andriani Didi Asmadi Erwan, Friesca Fadjrin, Sitti Fajar Okta Widarta Friesca Erwan Friesca Erwan Friesca Erwan Fuad Dwi Hanggara Habib Satria Hanifah, Maulia Rahmi Iing Pamungkas Ilyas Ilyas Ilyas Ilyas Ilyas Ilyas Irawan, Heri Tri Irawan, Risnadi Iskandar Hasanuddin Kasmawati Kasmawati Lestari, Rachmatika Lestari, Suci Ayu Lufika, Raihan Dara Mahmud Basuki Maliza, Noer Octaviana Muhammad Reza Aulia Nica Astrianda Noer Octaviana Maliza Noprita, Zikra Nova Nova Novianda, Dinda Octaviana Maliza, Noer Qadrinadia, Ivana Raihan Dara Lufika Ratu Cleopatra riza ulhaq Rosalina, Dian Safitriyawi, Roja Sentia, Prima Denny Sofiyanurriyanti Sugeng Santoso Suhendra, Rivansyah Suhendrianto Suhendrianto Suhendrianto Suhendrianto, Suhendrianto Sulaeman, Sarah Suryadi Suryadi Suryadi Suryadi Syahputra , Dedek Syahriza Syahriza Syamsiar, Syamsiar Teuku Andhika Malik Rahman Ulfa Syahputri Ulfah, Tya Wyona Allysha Rustandi Putri Yulia Annisa Zaty Fadhilla Zhaqiri, Hazdfi Zikra Noprita Zuhri, Sarika Zuhri, Sarika