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All Journal Journal Industrial Servicess 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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Metode Analisis Risiko Kerusakan Mesin Produksi di Indonesia: Literature Review Pamungkas, Iing; Irawan, Heri Tri; Basuki, Mahmud; Ridha, Arrazy Elba; Adib, Adib; Syahputra, Rizki Agam; Widarta, Fajar Okta
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Publisher : Universitas Teuku Umar

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Production machines are specially designed to perform various tasks such as processing, assembly, packaging and more depending on the type of production and the industry involved. However, damage to production machines is unavoidable with such a task. Machine damage can occur due to various factors, such as excessive use, inadequate maintenance, worn components, design errors and many more. Damage to the machine certainly raises risks, both measurable risks and immeasurable risks. The risk of machine failure can have a serious impact on the productivity, reliability and operating costs of a business. The use and application of risk analysis methods or approaches can be used to minimize the occurrence of excessive risk. The purpose of this research is to find out various methods of risk analysis of production machine damage in various industries in Indonesia. This research uses a systematic method by reviewing the literature from various published articles which are then identified and analyzed which focus on various production machine damage risk methods used in various industries in Indonesia. The results of the study show that there are several methods of analyzing the risk of damage to production machines that are commonly used in Indonesia, such as failure mode and effect analysis (FMEA, FMECA, fuzzy FMEA), risk based maintenance (RBM), fault tree analysis (FTA), fishbone diagram, enterprise risk management (ERM), failure tracking matrix (FTM), total quality control (TQM), logic tree analysis (LTA), overall equipment effectiveness (OEE), Hazard operability (Hazop), and certainty factor and forward chaining. Some of these methods are generally integrated with various other methods or approaches to obtain optimal results.
The application of machine learning algorithms for assessing the maturity level of palm fruits as the prominent commodity in the Western-Southern Area of Aceh Syahputra, Rizki Agam; Widarta, Fajar Okta
Operations Excellence: Journal of Applied Industrial Engineering Vol. 16, No. 1, (2024): OE March 2024
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/oe.2024.v16.i1.102

Abstract

The potential of palm oil plantations in Aceh is substantial, with the province ranking eighth in Indonesia for palm oil cultivation. Aceh boasts a vast oil palm plantation area of 470.8 thousand hectares, comprising 44% of Aceh's total plantation land. Palm fruit quality directly impacts palm oil production, emphasizing the need for consistent maturity levels. To address this, computer algorithms, especially machine learning, have been applied. This study introduces the Self-Organizing Map (SOM) Algorithm for palm fruit maturity determination. SOM's reliability in capturing dataset topology offers a diverse classification process, revolutionizing palm fruit maturity detection and optimizing palm oil production. This study uses 40 dataset consisted of 20 mature and 20 unmature palm fruit image as the basis data which then converted into RGB and HSV value with Matlab engine. The result of the study indicates that the SOM algorithm is capable of classifying the maturity detection with 100% precision result. The SOM algorithm is synthesized in a Graphical User Interface that is capable of reading and classifying the input data into the output cluster.
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.
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.
Peningkatan Kompetensi Pengelola Website melalui Pelatihan Berbasis Andragogi di Lingkungan Perguruan Tinggi Syahputra, Rizki Agam; Rosalina, Dian; Maliza, Noer Octaviana; Kasmawati, Kasmawati; Safitriyawi, Roja; Lestari, Suci Ayu; Ulfah, Tya
Jurnal Pengabdian kepada Masyarakat Nusantara Vol. 6 No. 2 (2025): Jurnal Pengabdian kepada Masyarakat Nusantara Edisi April - Juni
Publisher : Lembaga Dongan Dosen

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Abstract

Pelatihan pengelolaan dan pengembangan website di Universitas Teuku Umar bertujuan untuk meningkatkan kompetensi para pengelola website di lingkungan fakultas dan unit kerja universitas. Dalam era digital ini, pengelolaan website yang efektif sangat penting untuk memastikan informasi yang disampaikan selalu up-to-date, menarik, dan mudah diakses oleh mahasiswa, dosen, serta masyarakat umum. Pelatihan ini melibatkan peserta dari berbagai fakultas dan unit kerja yang bertanggung jawab atas pengelolaan website, dengan tujuan untuk memastikan bahwa setiap bagian terkait memiliki keterampilan yang seragam dalam mengelola konten website. Materi pelatihan mencakup pengelolaan konten dan penggunaan sistem WordPress sebagai platform pengelolaan website yang fleksibel dan mudah digunakan. Pelatihan ini juga membahas pengelolaan warna dan konten sesuai dengan standar yang ditetapkan oleh masing-masing instansi. Hasil dari pelatihan menunjukkan bahwa peserta mampu mengatasi tantangan yang dihadapi dalam mengelola website, serta dapat langsung mempraktikkan pengetahuan yang diperoleh, seperti pengeditan konten dan pembaruan tampilan website. Keberhasilan pelatihan ini diharapkan dapat mendukung proses Re-Akreditasi dan peralihan status Universitas Teuku Umar menuju Badan Layanan Umum (BLU), serta memberikan kontribusi positif dalam meningkatkan kualitas pengelolaan website universitas di masa mendatang. Pelatihan ini diharapkan dapat dilanjutkan secara berkala untuk memperkuat kapasitas pengelola website dalam menghadapi perkembangan teknologi digital.
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.  
Structural Equation Modeling Analysis of Purchase Behavior of Halal Products Zuhri, Sarika; Ilyas, Ilyas; Erwan, Friesca; Syahputra, Rizki Agam; Sentia, Prima Denny; Noprita, Zikra
Indonesian Journal of Halal Research Vol. 5 No. 1 (2023): February
Publisher : UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/ijhar.v5i1.20170

Abstract

Indonesia is renowned for being home to the largest Islamic population globally, but the absence of a halal certification logo on products continues to be a cause for concern. For Muslim customers, their basic knowledge of halal ingredients written on the product is the only guide they can be relied on. Hence, it is crucial to understand the essential factors that influence their purchasing behavior regarding halal-certified products. This study uses the Theory of Planned Behavior (TPB) to examine Muslim customers' behavior, as a case study, in Banda Aceh. The TPB questionnaire, including attitude, subjective norm, perceived behavioral control, and purchase intention attributes, was distributed online to respondents in the Banda Aceh area using non-probability random sampling. Through Structural Equation Modeling (SEM) methodology and AMOS software, this research reveals a strong correlation between attitude and subjective norm with Muslim customers' purchasing intention. Moreover, purchasing intention shows a strong relationship with purchasing behavior. Consequently, attitude, subjective norm, and customers' intention are the critical factors that influence Muslim customers' behavior when purchasing halal-certified products in Indonesia.
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 Fajar Okta Widarta Friesca Erwan Friesca Erwan Friesca Erwan Fuad Dwi Hanggara Habib Satria Hazdfi Zhaqiri Iing Pamungkas 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 Reza Aulia Mustafa Kamal Nica Astrianda Noer Octaviana Maliza Noprita, Zikra Nova Nova Novianda, Dinda Octaviana Maliza, Noer Putri Sarina Agustavia Qadrinadia, Ivana Raihan Dara Lufika Ratu Cleopatra Riski Arifin Risnadi Irawan riza ulhaq Rosalina, Dian Safitriyawi, Roja Sentia, Prima Denny Shaumy Fadhilla Sitti Fadjrin 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 Viktor Mawar Berutu Wyona Allysha Rustandi Putri Yulia Annisa Zaty Fadhilla Zuhri, Sarika Zuhri, Sarika