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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL REKAYASA TEKNOLOGI INFORMASI Abdimas Umtas : Jurnal Pengabdian kepada Masyarakat METIK JURNAL Jurnal Review Pendidikan dan Pengajaran (JRPP) Jurnal Informasi dan Teknologi Journal of Robotics and Control (JRC) Community Development Journal: Jurnal Pengabdian Masyarakat JURPIKAT (Jurnal Pengabdian Kepada Masyarakat) International Journal of Engineering, Science and Information Technology International Journal of Robotics and Control Systems KLIK: Kajian Ilmiah Informatika dan Komputer DiJITAC : Digital Journal of Information Technology and Communication Ilmu Komputer untuk Masyarakat Sanskara Akuntansi dan Keuangan West Science Social and Humanities Studies Jurnal Pengabdian Masyarakat dan Riset Pendidikan Scientific Journal of Engineering Research Journal of Computer Science Advancements Techno Agriculturae Studium of Research Research of Scientia Naturalis Journal of Multidisciplinary Sustainability Asean Jurnal Pengabdian Masyarakat Nusantara OBOR: Oikonomia Borneo Journal of Social Science Utilizing Technology Journal of Multidisciplinary Informatics, Artificial Intelligence, and Business Innovation
Aldi Bastiatul Fawait Fawait
Universitas Widya Gama Mahakam Samarinda
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USE OF ARTIFICIAL INTELLIGENCE IN PREDICTING ELECTRICITY NEEDS IN SMART CITIES Aldi Bastiatul Fawait; Zhang Li; Sara Hussain
Journal of Computer Science Advancements Vol. 3 No. 1 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v3i1.1620

Abstract

The rapid urbanization and adoption of smart city technologies have led to increasing complexities in managing electricity demand. Traditional methods of forecasting electricity needs often fail to accommodate the dynamic and real-time nature of energy consumption in smart cities. Artificial Intelligence (AI) offers a promising approach by leveraging machine learning algorithms and predictive analytics to address these challenges. This study explores the use of AI in predicting electricity needs, focusing on its applicability in optimizing energy distribution and reducing inefficiencies in smart city infrastructures. The research aims to develop an AI-based predictive model to forecast electricity demand using historical and real-time data. The methodology involves data collection from smart meters, weather forecasts, and demographic records, followed by training machine learning algorithms such as Random Forest, Support Vector Machines, and Neural Networks. Performance metrics, including prediction accuracy, computational efficiency, and scalability, were analyzed to evaluate the model's effectiveness. Results indicate that AI-based models outperform traditional forecasting methods, achieving an average prediction accuracy of 92%. Neural Networks demonstrated the highest performance, particularly in handling complex and nonlinear data patterns. The AI model also showcased scalability by adapting to increasing datasets without significant degradation in performance. The study concludes that AI is a transformative tool for predicting electricity needs in smart cities. By enhancing forecast accuracy and enabling efficient energy distribution, AI contributes to sustainable urban development and smarter energy management systems.
The Role of Applied Statistics in Drug Development and Clinical Trials Sitti Rahmah; Aldi Bastiatul Fawait; Dadang Muhammad Hasyim
Research of Scientia Naturalis Vol. 1 No. 6 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/scientia.v1i6.1584

Abstract

Background: The integration of applied statistics in drug development and clinical trials is essential for ensuring the efficacy and safety of new pharmaceuticals. Statistical methods play a critical role in designing studies, analyzing data, and interpreting results, thereby influencing regulatory decisions and clinical practices. Objective: This study aims to examine the role of applied statistics in the drug development process, particularly within clinical trials. The focus is on identifying key statistical techniques and their impact on trial outcomes and decision-making. Methodology: A comprehensive review of literature was conducted, analyzing various statistical methods employed in clinical trials, including sample size determination, randomization techniques, and data analysis methods. Case studies were included to illustrate the application of these methods in real-world scenarios. Results: Findings indicate that robust statistical methodologies significantly improve the reliability of clinical trial results. Proper sample size calculations ensure adequate power to detect treatment effects, while randomization techniques minimize bias. Additionally, advanced data analysis methods enhance the interpretation of trial outcomes, leading to more informed regulatory approvals. Conclusion: This research highlights the indispensable role of applied statistics in drug development and clinical trials. Emphasizing the importance of sound statistical practices not only improves trial integrity but also contributes to the overall success of new drug therapies. Continued advancements in statistical methods will further enhance the efficiency and effectiveness of clinical research.
Implementation of Artificial Intelligence in Cybersecurity Crisis Management Aldi Bastiatul Fawait; La Jupriadi Fakhri; Virasanty Muslimah
Journal of Multidisciplinary Sustainability Asean Vol. 1 No. 6 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/ijmsa.v1i6.1776

Abstract

Background. The growing complexity of cybersecurity threats has led to an increasing demand for faster and more efficient solutions. As cyber threats evolve in sophistication, the implementation of Artificial Intelligence (AI) in cybersecurity crisis management has become highly relevant. AI’s ability to process vast amounts of data quickly and detect patterns that may be undetectable to human operators offers significant potential in combating cybercrime and cyberattacks. Purpose. This study aims to evaluate how AI can enhance the effectiveness of cybersecurity by improving the detection and response to cyber threats. Specifically, the research focuses on understanding AI's role in identifying potential threats more quickly and responding with greater efficiency compared to traditional methods. Method. The research employs a mixed-method approach, combining quantitative data analysis and qualitative interviews. Quantitative data were gathered from cyberattack simulations to measure AI’s effectiveness in detecting and responding to various types of cyber threats. Additionally, qualitative interviews were conducted with cybersecurity experts to gather insights into AI’s practical applications and limitations in real-world scenarios. Results. The findings show that AI significantly accelerates threat detection, improving the overall response efficiency with a success rate of up to 85%. AI is also capable of analyzing large datasets in a short period, enabling faster identification of vulnerabilities and potential threats. However, AI still faces limitations in handling unexpected and novel types of cyberattacks, indicating that it cannot entirely replace human expertise. Conclusion. While AI offers numerous advantages in the field of cybersecurity, it must be integrated with human expertise to address its limitations effectively. AI technology should be continuously updated to adapt to emerging threats. This study contributes to the understanding of AI’s strategic role in cybersecurity and provides valuable direction for further research aimed at overcoming the technology’s weaknesses in threat management.
Implementation of Data Mining Using Simple Linear Regression Algorithm to Predict Export Values Aldi Bastiatul Fawait; Sitti Rahmah; Apolonia Diana Sherly da Costa; Nazaruddin Insyroh; Asno Azzawagama Firdaus
Scientific Journal of Engineering Research Vol. 1 No. 1 (2025): March
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v1i1.2025.11

Abstract

This study aims to analyze the trends in export value in East Kalimantan. The research utilizes secondary data sourced directly from the Central Statistics Agency of East Kalimantan Province. A simple linear regression algorithm for data mining is employed as the analytical method. The findings indicate a decline in East Kalimantan's export value from January 2022 to April 2024, as well as in the forecasted export value from May 2024 to December 2024. The prediction model achieved a Root Mean Square Error (RMSE) value of 3.182%, demonstrating a high level of accuracy in estimating export values. This research is expected to serve as a valuable reference for stakeholders in formulating strategies to enhance East Kalimantan's export performance and contribute to the region's future economic development.
The Influence of Social Media in Increasing Student Motivation in Mathematics Lessons for Elementary Schools Aisyah Nursyam; Reviandari Widyatiningtyas; Hersiyati Palayukan; Aldi Bastiatul Fawait
Journal of Social Science Utilizing Technology Vol. 2 No. 1 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jssut.v2i1.855

Abstract

Background. Mathematics learning in elementary schools (SD) often requires innovative approaches to increase student engagement. With the development of technology, the use of social media as a learning tool is starting to become the focus of research to increase students' learning motivation in mathematics. Purpose. The research aims to measure how much influence the use of social media has in increasing students' motivation in learning mathematics in elementary school, as well as providing a concrete understanding of the relationship between social media and motivation to learn mathematics. Method. A quantitative approach using a survey model is used to examine the impact of social media on student motivation. Questionnaires were distributed to elementary school teachers and mathematics education students via Google Form and WhatsApp (WA) groups. Research ethical principles were upheld, and data were analyzed using Miles and Huberman's qualitative data analysis techniques. Results. The majority of respondents gave a positive view of the influence of social media in mathematics learning. However, there are a small number who feel that social media can interfere with studying concentration. The integration of social media needs to be considered wisely to minimize risks and maximize its benefits in mathematics learning. Conclusion. The research results show that social media has great potential in increasing students' motivation in learning mathematics in elementary school. However, its use needs to be managed wisely and responsive to student needs and preferences. Social media integration can be an interesting and effective learning alternative in the context of mathematics learning at school.
Performance Analysis of Ensemble Learning Models Comparing Bagging and Boosting Techniques for Early Preeclampsia Risk Detection in Pregnant Women Prediction Yudhi Saputra; Milkhatun Milkhatun; Aldi Bastiatul Fawait; Zakaria Ahmad Dahlan; Yazeed Al Moaiad; Haviluddin Haviluddin; Rayner Alfred
International Journal of Engineering, Science and Information Technology Vol 6, No 1 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i1.1880

Abstract

Preeclampsia is a major pregnancy complication that substantially contributes to maternal morbidity and mortality worldwide, making early identification of risk factors essential for effective prevention and timely clinical intervention. This study evaluates the performance of ensemble learning models by comparing bagging and boosting techniques to develop an accurate early prediction system for preeclampsia risk using clinical medical record data. The research follows the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework, encompassing data understanding, data preparation, modeling, evaluation, and interpretation. The dataset was obtained from RSUD Inche Abdoel Moeis Samarinda and underwent preprocessing procedures, including data cleaning, transformation, feature encoding, normalization, and Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance. Six ensemble learning algorithms were evaluated, consisting of Random Forest, Extra Trees, and Rotation Forest as bagging methods, and XGBoost, LightGBM, and CatBoost as boosting methods. Model performance was assessed using accuracy, precision, recall, and weighted F1-score. The experimental results demonstrate that Random Forest achieved the highest predictive performance, with an accuracy of 0.92, precision of 0.93, recall of 0.92, and weighted F1-score of 0.91, indicating superior robustness and generalization capability. Extra Trees achieved comparable accuracy (0.92) but exhibited lower prediction stability across evaluation metrics. Among the boosting algorithms, LightGBM and CatBoost each obtained an accuracy of 0.89, while XGBoost achieved 0.88. Rotation Forest recorded the lowest accuracy (0.62), suggesting limited suitability for this clinical dataset. These findings indicate that bagging-based ensemble methods, particularly Random Forest, outperform boosting techniques for imbalanced clinical data and provide strong empirical support for developing reliable Clinical Decision Support Systems (CDSS) for early preeclampsia screening and risk assessment in healthcare settings
Perbandingan Algoritma Nearest Neighbor dan Cheapest Insertion Heuristic Dalam Menyelesaikan Pendistribusian Barang SPX Express Shopee Sitti Rahmah; Yulindawati Yulindawati; Muh Jamil; Aldi Bastiatul Fawait
Jurnal Rekayasa Teknologi Informasi (JURTI) Vol 10, No 2 (2026): Jurnal Rekayasa Teknologi Informasi (JURTI)
Publisher : Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/jurti.v10i2.26545

Abstract

Kajian ini membahas perbandingan algoritma Nearest Neighbor dan Cheapest Insertion Heuristic dalam menyelesaikan permasalahan pendistribusian barang pada SPX Express Shopee. Permasalahan distribusi barang sering dihadapi oleh perusahaan logistik karena banyaknya titik tujuan yang harus dikunjungi sehingga diperlukan penentuan rute yang efisien untuk meminimalkan jarak tempuh. Dalam riset dirancang untuk menentukan rute distribusi optimal dengan membandingkan kinerja kedua algoritma tersebut. Metode riset yang digunakan adalah metode komputasi heuristik berdasarkan data jarak antartitik distribusi. Proses studi dimulai dengan menentukan titik awal distribusi, menghitung jarak antar lokasi, kemudian menerapkan algoritma Nearest Neighbor dan Cheapest Insertion Heuristic untuk memperoleh rute penyaluran yang efisien.Temuan riset menunjukkan bahwa algoritma Nearest Neighbor menghasilkan dua alternatif rute dengan total jarak 102 km dan 107,3 km. Sementara itu, algoritma Cheapest Insertion Heuristic menunjukkan rute dengan total jarak perjalanan yang lebih pendek sebesar 95,4 km. Berdasarkan hasil tersebut, disimpulkan bahwa metode Cheapest Insertion Heuristic lebih optimal dalam menentukan rute pendistribusian barang karena mampu meminimalkan total jarak tempuh. Hasil riset diharapkan dapat memberikan referensi penentuan rute distribusi yang lebih efisien pada kegiatan logistik bagi pihak terkait dalam mengambil keputusan.
Perbandingan Akurasi Algoritma Machine Learning untuk Prediksi Diabetes Berdasarkan Data Skrining Pasien Lansia di Puskesmas Sempaja Nelson Sompa Arifin; Yohanes Andriano Teras; Gede Enos Karli; Norsianalara; Lisnawati; Aldi Bastiatul Fawait
Journal of Multidisciplinary Informatics, Artificial Intelligence, and Business Innovation Vol. 1 No. 2 (2026)
Publisher : Universitas Widya Gama Mahakam Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24903/minabis.v1i2.25

Abstract

Diabetes melitus merupakan salah satu penyakit tidak menular yang memerlukan deteksi dini untuk mengurangi risiko komplikasi dan meningkatkan kualitas hidup pasien. Penelitian ini bertujuan membandingkan performa beberapa algoritma machine learning dalam memprediksi diabetes berdasarkan data skrining pasien lansia di Puskesmas Sempaja. Metode yang digunakan adalah data mining dengan tahapan Knowledge Discovery in Databases (KDD), meliputi seleksi data, prapemrosesan data, transformasi data, pemodelan, dan evaluasi. Dataset dibagi menjadi 80% data latih dan 20% data uji. Algoritma yang digunakan meliputi Decision Tree, K-Nearest Neighbors (KNN), Random Forest, Naïve Bayes, Support Vector Machine (SVM), dan Logistic Regression. Evaluasi model dilakukan menggunakan metrik accuracy, precision, recall, dan F1-score. Hasil penelitian menunjukkan bahwa Naïve Bayes, SVM, dan Logistic Regression memperoleh performa terbaik dengan nilai akurasi sebesar 88% dan F1-score sebesar 83%. Sementara itu, Decision Tree menunjukkan kemampuan yang lebih baik dalam mendeteksi kelas positif dibandingkan model lainnya. Hasil penelitian menunjukkan bahwa algoritma machine learning dapat dimanfaatkan untuk mendukung prediksi diabetes, dengan Naïve Bayes, SVM, dan Logistic Regression sebagai model yang memiliki performa terbaik pada dataset yang digunakan.
ANALISIS SENTIMEN ULASAN PRODUK E-COMMERCE MENGGUNAKAN NAIVE BAYES Leo nakanisi aran; Andrian Anye; Sopia daud; Vinsensia Florince Seke; Aldi Bastiatul Fawait
Journal of Multidisciplinary Informatics, Artificial Intelligence, and Business Innovation Vol. 1 No. 2 (2026)
Publisher : Universitas Widya Gama Mahakam Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24903/minabis.v1i2.26

Abstract

The rapid growth of e-commerce in Indonesia has significantly increased the number of customer reviews on marketplace platforms, particularly Tokopedia. These reviews contain valuable customer opinions that can be utilized to evaluate product quality and service performance. However, the large volume of reviews makes manual analysis inefficient and time-consuming. This study aims to implement the Multinomial Naive Bayes algorithm for sentiment analysis of Tokopedia product reviews to automatically classify customer opinions into positive and negative sentiments. The dataset used consists of 500 labeled customer reviews obtained from Kaggle. The research process includes text preprocessing through case folding and cleaning, feature extraction using Term Frequency–Inverse Document Frequency (TF-IDF) with max_features=3000 and ngram_range=(1,2), followed by data splitting into 80% training data and 20% testing data. The classification process is performed using the Multinomial Naive Bayes algorithm. Model performance is evaluated using Accuracy, Precision, Recall, F1-Score, and Confusion Matrix. The experimental results achieved an accuracy of 83%, indicating that the combination of preprocessing techniques, TF-IDF feature extraction, and the Multinomial Naive Bayes algorithm is effective in classifying customer sentiments on Tokopedia product reviews and can support the evaluation of product quality and service performance in e-commerce platforms.
Implementasi Metode Simple Additive Weighting dalam Seleksi Penerima Beasiswa Aulita Az'Zahra Dhena Aldy; Elsya Fauziah; Hendrikus Hang Himang; Ferdinandus Heru Moreno Christian Paran; Maria Kristiana Damayanti Teting; Aldi Bastiatul Fawait
Journal of Multidisciplinary Informatics, Artificial Intelligence, and Business Innovation Vol. 1 No. 2 (2026)
Publisher : Universitas Widya Gama Mahakam Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24903/minabis.v1i2.27

Abstract

Abstract: The scholarship selection process requires a method that can support objective decision-making based on multiple criteria. This study applies the Simple Additive Weighting (SAW) method to determine scholarship recipients based on four criteria: Grade Point Average (GPA), parents' income, number of family dependents, and student achievements. The dataset consisted of 80 student alternatives. The research process included normalization, weighting, preference value calculation, and alternative ranking. The results showed that preference values ranged from 0.56733 to 0.96298, with an average value of 0.73029. Alternative A70 obtained the highest preference value of 0.96298 and was recommended as the most eligible scholarship recipient. In addition, 53 students (66.25%) achieved preference values above 0.70. The findings indicate that the SAW method was successfully implemented and is capable of producing a more objective, transparent, and measurable scholarship selection process. Abstrak: Proses seleksi penerima beasiswa memerlukan metode yang mampu membantu pengambilan keputusan secara objektif berdasarkan berbagai kriteria. Penelitian ini menerapkan metode Simple Additive Weighting (SAW) untuk menentukan calon penerima beasiswa berdasarkan empat kriteria, yaitu IPK, penghasilan orang tua, jumlah tanggungan keluarga, dan prestasi mahasiswa. Data yang digunakan terdiri dari 80 mahasiswa sebagai alternatif. Proses penelitian dilakukan melalui tahapan normalisasi, pembobotan, perhitungan nilai preferensi, dan perangkingan alternatif. Hasil penelitian menunjukkan bahwa nilai preferensi berada pada rentang 0,56733 hingga 0,96298 dengan rata-rata sebesar 0,73029. Alternatif A70 memperoleh nilai preferensi tertinggi sebesar 0,96298 sehingga direkomendasikan sebagai kandidat penerima beasiswa yang paling layak. Selain itu, sebanyak 53 mahasiswa (66,25%) memperoleh nilai preferensi di atas 0,70. Hasil penelitian menunjukkan bahwa metode SAW berhasil diterapkan dalam proses seleksi penerima beasiswa dan mampu menghasilkan keputusan yang lebih objektif, transparan, dan terukur.
Co-Authors Abiyajid Bustami Agry Alfiah Ahmad Fadilla Aisyah Nursyam Alfian Ma’arif ALYA MASITHA Andi Hasyim Andrian Anye Anton Yudhana Apolonia Diana Sherly da Costa Arief Yanto Rukmana Arifin, Merlina Lidiana Asno Azzawagama Firdaus Asno Azzawagama Firdaus Aulita Az'Zahra Dhena Aldy Bayu Pamungkas Bernardo Damian De Ornay Cecillia Listia Anggraini Dadang Muhammad Hasyim Darmun, Darmun Dedi Zulkarnain Pulungan Dodi Irawan Dony Andrasmoro Edwin Pramudya Eko Prasetio Widhi Elsya Fauziah Fahmi, Miftahuddin Farida Arinie Soelistianto Ferdinandus Heru Moreno Christian Paran Feri Adriyanto Furizal Furizal Furizal, Furizal Gede Enos Karli Gregorivo Hizkia Brighita Totopandey Haviluddin Haviluddin Hendratri, Bhaswarendra Guntur Hendrikus Hang Himang Hersiyati Palayukan Hidayatus Sibyan Huda, Syafa'at Ariful Jamil, Muh Jamil, Muh Jamil Judijanto, Loso Kariyamin, Kariyamin Klara Bare Nuhan Kohar , Abdul La Jupriadi Fakhri La Jupriadi Fakhri Leo nakanisi aran Lisnawati Loso Judijanto M. Fajar Rizky Maghfiroh, Hari Mahmoud Ahmad Al-Khasawneh Marcello Fellix Febrian Mardiati Mardiati Maria Kristiana Damayanti Teting Maslim Tammaling Merlina Lidiana Arifin Merlina Lidiana Arifin Milkhatun Milkhatun, Milkhatun Muh. Jamil Muhamad Fuat Asnawi Muhammad Kunta Biddinika Nadia Keril Saputri Nazaruddin Insyroh Nelson Sompa Arifin Nelson Sompa Arifin Norsianalara Nursalim Nursyam, Aisyah Otniel Christovel Ganda Puteri Aprilani Rahmah, Sitti Rahmah Rahmawati Ramelan, Agus Rayner Alfred Reviandari Widyatiningtyas, Reviandari Reyza Febrianto Ridho Aulia Tabliq Sidiq Rizky Wardhani Ronald Alexandre Rosmasari Rosmasari, Rosmasari Rusdi Umar Rywalman Rante Pasang Saputra, Yudhi Fajar Saputri, Nadia Keril Sara Hussain Sherly Virgoila Pidang Sitti Rahmah Sitti Rahmah Sitti Rahmah Sitti Rahmah Sitti Rahmah Sitti Rahmah Sitti Rahmah Rahmah Sopia daud Sri Nur Hidayati Sugiarto Sugiarto Sugiarto S Sulung Alfianto Akbar Sunardi, Sunardi Suwarno, Iswanto Syaifullah, Ahmad Syekh Budi Syam Thitus Gilaa Vann Sok Vinsensia Florince Seke Virasanty Muslimah Wartono, Tono wati, asiah Yana Mulyana Yazeed Al Moaiad Yazeed Al Moaiad Yohanes Andriano Teras Yovi Aldiyanto Yudhi Fajar Saputra Yudhi Fajar Saputra Yudhi Fajar Saputra Yudhi Saputra Yulindawati Yulindawati, Yulindawati Yusriati, Yusriati Zakaria Ahmad Dahlan Zhang Li