Article Per Year (5 Year)
p-Index From 2021 - 2026
3.534
P-Index
This Author published in this journals
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

Published : 24 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 24 Documents
Search

 1   2   3 
Leveraging Machine Learning for Sentiment Analysis in Hotel Applications: A Comparative Study of Support Vector Machine and Random Forest Algorithms Suryadi, Suryadi; Syahputra , Dedek; Astrianda, Nica; Syahputra, Rizki Agam; Suhendra, Rivansyah
Brilliance: Research of Artificial Intelligence Vol. 4 No. 2 (2024): Brilliance: Research of Artificial Intelligence, Article Research November 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i2.4877

Abstract

This research aims to conduct sentiment analysis on user reviews of hotel booking applications such as Trivago, Tiket, Booking, Traveloka, and Agoda, collected from the Google Play Store. The dataset used consists of 5,000 user reviews, with 80% of the data allocated for training and 20% for testing. Two algorithms applied in this study are Support Vector Machine (SVM) and Random Forest, with performance evaluation based on accuracy, precision, recall, and F1-score metrics. The test results show that the Random Forest algorithm delivers the best performance on the Trivago application with 94% accuracy, 94% precision, 100% recall, and a 97% F1-score. Random Forest proves to be more effective in handling diverse review data, while the Support Vector Machine (SVM) algorithm also produces good results in sentiment classification. This research contributes to the development of sentiment analysis based on user reviews, which can be utilized by app developers and hotel management to improve service quality and user experience.
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.
Strategi Peningkatan Kesadaran Data dan Informasi Masyarakat di Era Digital Syahputra, Rizki Agam; Octaviana Maliza, Noer; Kasmawati, Kasmawati; Aulia Putri, Cut Widy
Jurnal Pengabdian kepada Masyarakat Nusantara Vol. 5 No. 3 (2024): Jurnal Pengabdian kepada Masyarakat Nusantara (JPkMN) Edisi Mei- Agustus
Publisher : Lembaga Dongan Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55338/jpkmn.v5i3.3543

Abstract

Di era digital yang semakin terkoneksi, akses terhadap informasi menjadi lebih mudah. Namun, tantangan baru muncul terkait kesadaran data dan informasi di masyarakat. Era digital menciptakan lautan informasi tak terbatas dengan jumlah data yang meningkat eksponensial. Kurangnya kesadaran dapat mengakibatkan kesulitan membedakan informasi yang sahih dari yang palsu, serta rentan terhadap penyebaran informasi palsu. Peran teknologi media sosial memperumit dinamika informasi, menjadi platform utama berbagi informasi dan mendukung perkembangan bisnis. Pandemi COVID-19 merubah tata kelola aktivitas sehari-hari yang sangat bergantung pada ekosistem digital. Kesadaran data dan informasi berdampak pada aspek sosial, ekonomi, dan politik. Strategi pendidikan masyarakat penting untuk meningkatkan kesadaran data dan informasi. Melalui pemahaman lebih baik, strategi pendidikan dapat membantu masyarakat menghadapi tantangan informasi di era digital ini dengan lebih baik. Kegiatan pengabdian tersebut dilaksanakan pada 16 September 2023, yang dilaksanakan melalui kerjasama dengan Integrated School di Aceh Besar, Prov. Aceh, Indonesia. Pengabdian masyarakat ini dilakukan dengan metode workshop yang disertai studi kasus dalam persiapan menghadapi disinformasi.  Hasil yang diperoleh pada pengabdian diketahui bahwa masih minimnya literasi digital yang dimiliki oleh masyarkat, dan dengan adanya kegiatan ini diharapkan mampu meningkat kesadaran dan kewaspadaan dalam dunia digital.
Designing a University Batik Motif Based on User Preferences using Conjoint Analysis Zuhri, Sarika; Erwan, Friesca; Ilyas, Ilyas; Suhendrianto, Suhendrianto; Syahputra, Rizki Agam; Cleopatra, Ratu
Dinamika Kerajinan dan Batik: Majalah Ilmiah Vol. 40 No. 2 (2023): DINAMIKA KERAJINAN DAN BATIK : MAJALAH ILMIAH
Publisher : Balai Besar Standardisasi dan Pelayanan Jasa Industri Kerajinan dan Batik

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22322/dkb.v40i2.8042

Abstract

Batik is one of Indonesia favourite outwears and generally used in formal and semiformal events. This study aims to obtain a university batik motif based on its user preferences, which involving Acehnese motif. This study used stratified random sampling technique to determine 186 batik users who involved as the respondent of this study. The users contributed their perspectives by filling up two steps of questionnaire. The first questionnaire aims to identify user preferences on the design attributes, which consist of logo, the position of logo, Aceh batik motif, fabric basic colour, batik motif colour, and additional batik motif. The second questionnaire aims to obtain the most preference of batik design according to the combination of the attributes. Data obtained from the questionnaires will be analysed using conjoint analysis through SPSS® 22 This study revealed 16 combinations of batik design based on user preferences and the main important attribute in choosing the design was the position of the university logo. Amongst the 16 combinations of batik design, one design shows significant utility value as of 0.543 and determine as the most preferable design chosen by the university batik users.  
Co-Authors Abdiel Khaleil Akmal Adib Adib Adib Adib, Adib Andriansyah Arifin, Riski Arrazy Elba Ridha Aulia Putri, Cut Widy Chairil Akbar Cleopatra, Ratu Cut Widy Aulia Putri Dewi Andriani Didi Asmadi Erwan, Friesca Fadjrin, Sitti Fajar Okta Widarta Friesca Erwan Friesca Erwan Fuad Dwi Hanggara Habib Satria Hanifah, Maulia Rahmi Iing Pamungkas 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 Iqbal 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 Zuhri, Sarika Zuhri, Sarika