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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Jurnal E-Komtek Jurnal Teknik Informatika (JUTIF) Jurnal Pengabdian Masyarakat : Pemberdayaan, Inovasi dan Perubahan Jurnal Sistem Informasi Galuh
Firmansyah, Muhammad Raafi'u
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Religion Automotive Engineering Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mechanical Engineering Public Health Other

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Analisa Slope Wilayah Kebakaran Hutan menggunakan Metode Naive Bayes Lisda; Isnaeni, Nenen; Firmansyah, Muhammad Raafi'u
Jurnal Sistem Informasi Galuh Vol 2 No 2 (2024): Journal of Galuh Information Systems
Publisher : Fakultas Teknik Jurusan Sistem Informasi Universitas Galuh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25157/jsig.v2i2.3934

Abstract

Forest and land fires are an economic and environmental problem that can cause serious damage. We can predict what factors cause forest fires. It is undeniable that topographical conditions affect the triggering and propagation of fires. The topographical condition itself is in the form of a slope, where fire propagation will be faster when going up the slope than going down the slope. This study aims to match whether the slope and display locations that are prone to the spread of certain fires with high fire intensity actually have a high fire potential and report the magnitude of the influence of the slope in the prediction of fire potential. One of the common approaches to classifying data is to use data mining. So in this study the researchers used the Naive Bayes Classifier as a classification method by getting the highest accuracy value of 0.99%.
Optimization of Recruitment of Prospective Financial Managers Through a Decision Support System with the Multi Attribute Utility Theory Method Firmansyah, Muhammad Raafi'u; Sahara, Sahara; Yeyi Gusla Nengsih; Dedi Rahman Habibie; Yohani Setiya Rafika Nur
Jurnal E-Komtek (Elektro-Komputer-Teknik) Vol 8 No 1 (2024)
Publisher : Politeknik Piksi Ganesha Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37339/e-komtek.v8i1.1767

Abstract

This study evaluates the application of Multi Attribute Utility Theory (MAUT)-based decision support systems in the recruitment process of financial managers in technology companies "TechInnovate". This study aims to identify the extent to which the use of MAUT can increase objectivity and effectiveness in candidate selection. The quantitative method was used by collecting data from 50 candidates who volunteered for this position. The assessment criteria that have been determined include Education and Qualifications, Work Experience, Analytical and Financial Skills, Communication Skills, Leadership and Team Management, Ethics and Integrity, Technology Capabilities, Adaptability and Innovation, Decision Making Ability. Data is collected through scores assigned to each criterion for each candidate, which are then aggregated taking into account the relative weights of each criterion. Statistical analysis is applied to evaluate the distribution of scores and the effectiveness of the system in selecting the most suitable candidates. Results from this study show that the application of MAUT is significant in improving objectivity in recruitment decision making, with high scores strongly correlated with expected job performance in in-depth interviews and follow-up evaluations. This study proves that integrating a systematic approach in recruitment not only strengthens the selection process but also promotes data-driven and objective decisions. The implications of this research are relevant for companies operating in dynamic and technology-centric environments, suggesting the integration of MAUT-based decision support systems as an effective strategy in the recruitment process.
Elevasi Brand “LIMPUS” Melalui Kreativitas Digital: Program Pelatihan Konten Media Sosial dan Video Promosi Aldo, Dasril; Nur, Yohani Setiya Rafika; Lishobrina, Lina Fatimah; Firmansyah, Muhammad Raafi'u; Sulaeman, Gilang; Fathoni, M. Yoka
Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Vol 7, No 3 (2024): Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstormin
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/japhb.v7i3.6786

Abstract

Dalam upaya meningkatkan kesadaran dan partisipasi masyarakat terhadap literasi serta pelestarian lingkungan melalui platform digital, program "Elevasi Brand 'Limbah Pustaka' Melalui Kreativitas Digital" telah dilaksanakan. Program dua hari ini dirancang untuk mengasah keterampilan digital para peserta, meliputi pengenalan dan praktik pembuatan konten media sosial dan video. Hari pertama fokus pada pengenalan YouTube, podcast, dan peran content creator, dilanjutkan dengan praktik pembuatan channel YouTube dan pengelolaan video yang efektif, termasuk pengambilan dan editing video. Hari kedua mengedepankan pentingnya kesadaran lingkungan melalui media digital, pengenalan Canva, serta praktik pembuatan poster dan konten visual lainnya. Hasil dari program ini menunjukkan peningkatan signifikan dalam kemampuan peserta dalam mengelola konten digital yang kreatif dan informatif, sekaligus meningkatkan branding dan visibilitas 'Limbah Pustaka' dalam masyarakat luas. Program ini menunjukkan bahwa dengan pelatihan yang tepat, teknologi digital dapat menjadi alat yang efektif untuk promosi dan edukasi publik terkait isu-isu penting seperti literasi dan pelestarian lingkungan.
Peningkatan Gaya Hidup Sehat Anak melalui Edukasi Pencegahan Diabetes Berbasis Multimedia Interaktif di Purbalingga Paramadini, Adanti Wido; Aldo, Dasril; Nur, Yohani Setiya Rafika; Firmansyah, Muhammad Raafi'u; Sa'adah, Aminatus; Fathan, Faizal Burhani Ulil; Sulaeman, Gilang; Faiz, M. Hanif Al; Hidayat, Afifah Naurah; Maulana, Ihsan; Fau, Andrew; Yasin, Feri; Suprapto, Amelia Rut; Muadin, Dika Alim
Jurnal Pengabdian Masyarakat: Pemberdayaan, Inovasi dan Perubahan Vol 5, No 4 (2025): JPM: Pemberdayaan, Inovasi dan Perubahan
Publisher : Penerbit Widina, Widina Media Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59818/jpm.v5i4.1685

Abstract

The incidence of type 2 diabetes mellitus among children is rising due to poor dietary habits and lack of physical activity from an early age. Health education remains suboptimal, especially in areas with limited access to health information. This community service activity aimed to raise awareness and understanding among elementary school children and their parents regarding early diabetes prevention through multimedia-based educational technology. The method used included interactive counseling, sugar content demonstrations in food, introduction of the Diabetes Detective educational app, and blood glucose screening using a digital glucometer. The activity took place in Muntang Village, Purbalingga, involving 30 children and 25 parents. Results showed an increase in children’s understanding from 49.5% (pre-test) to 85.5% (post-test), while 92% of parents stated the media was easy for children to understand. Children’s average blood glucose level was normal (92.5 mg/dL), while four parents were in the prediabetic range. The activity demonstrates that an interactive, contextual educational approach can enhance health literacy and promote healthy habits in families. This model can be replicated as a preventive strategy using digital technology in other regions.ABSTRAKKasus diabetes mellitus tipe 2 pada anak-anak meningkat seiring pola makan buruk dan kurangnya aktivitas fisik sejak dini. Edukasi mengenai gaya hidup sehat masih belum optimal, terutama di daerah dengan akses informasi kesehatan terbatas. Kegiatan pengabdian ini bertujuan meningkatkan kesadaran dan pemahaman anak-anak sekolah dasar serta orang tua mengenai pencegahan dini diabetes melalui media edukasi berbasis teknologi multimedia. Metode yang digunakan adalah penyuluhan interaktif, demonstrasi kandungan gula pada makanan, pengenalan aplikasi edukatif Diabetes Detective, dan pemeriksaan gula darah menggunakan glukometer digital. Kegiatan dilaksanakan di Desa Muntang, Purbalingga, dengan melibatkan 30 anak dan 25 orang tua. Hasil menunjukkan peningkatan pemahaman anak dari rata-rata 49,5% (pre-test) menjadi 85,5% (post-test), dan 92% orang tua menyatakan media mudah dipahami. Rata-rata kadar gula darah anak normal (92,5 mg/dL), sedangkan empat orang tua berada pada kategori prediabetes. Kegiatan ini membuktikan bahwa pendekatan edukatif yang interaktif dan kontekstual dapat meningkatkan literasi kesehatan anak dan keluarga serta dapat direplikasi sebagai strategi preventif berbasis teknologi di wilayah lain.
Multivariate Forecasting of Paddy Production: A Comparative Study of Machine Learning Models Yasin, Feri; Firmansyah, Muhammad Raafi'u; Aldo, Dasril; Amrustian, Muhammad Afrizal
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 3 (2025): JUTIF Volume 6, Number 3, Juni 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.3.4681

Abstract

Accurate rice production forecasting plays an important role in supporting national food security planning. This study aims to evaluate the performance of four machine learning algorithms, namely Random Forest, XGBoost, Support Vector Regression (SVR), and Linear Regression, in predicting three target variables simultaneously: harvest area, productivity, and production. The dataset used includes annual data per province in Indonesia from 2018 to 2024 obtained from the Central Statistics Agency (BPS). Evaluation was conducted using five metrics: MAE, RMSE, MAPE, R², and training time. The results of the experiment showed that the Random Forest Regressor performed best in the 80:20 scenario, with an MAE of 76,259.52, an RMSE of 154,036.91, a MAPE of 0.61%, and an R² of 0.997. XGBoost showed a competitive performance with an MAE of 79,381.44 and faster training times. In contrast, the SVR showed the worst performance with the MAPE reaching 198.56% and the R² of 0.209. Linear Regression as baseline recorded an MAE of 1,194,355.28 and an R² of 0.503, indicating that the linear model is not effective enough for this data. The 80:20 scenario is considered the best configuration because it is able to balance the accuracy and generalization of the model. These findings show that the use of ensemble algorithms, especially Random Forest and XGBoost, has the potential to be applied practically by agricultural agencies or local governments in designing data-driven policies for more proactive and predictive rice production management. Furthermore, this study contributes to the advancement of applied informatics by demonstrating how machine learning models can be effectively used in multivariate forecasting for complex, real-world problems, thereby supporting the development of intelligent decision-support systems in the agricultural domain.
Exploring bibliometric trends in speech emotion recognition (2020-2024) Rosita, Yesy Diah; Firmansyah, Muhammad Raafi'u; Utami, Annisaa
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp3421-3434

Abstract

Speech Emotion Recognition (SER) is crucial in various real-world applications, including healthcare, human-computer interaction, and affective computing. By enabling systems to detect and respond to human emotions through vocal cues, SER enhances user experience, supports mental health monitoring, and improves adaptive technologies. This research presents a bibliometric analysis of SER based on 68 articles from 2020 to early 2024. The findings show a significant increase in publications each year, reflecting the growing interest in SER research. The analysis highlights various approaches in preprocessing, data sources, feature extraction, and emotion classification. India and China emerged as the most active contributors, with external funding, particularly from the NSFC, playing a significant role in the advancement of SER research. SVM remains the most widely used classification model, followed by KNN and CNN. However, several critical challenges persist, including inconsistent data quality, cross-linguistic variability, limited emotional diversity in datasets, and the complexity of real-time implementation. These limitations hinder the generalizability and scalability of SER systems in practical environments. Addressing these gaps is essential to enhance SER performance, especially for multimodal and multilingual applications. This study provides a detailed understanding of SER research trends, offering valuable insights for future advances in speech-based emotion recognition.
Co-Authors Adanti Wido Paramadini Dasril Aldo Dedi Rahman Habibie Faiz, M. Hanif Al Fathan, Faizal Burhani Ulil Fau, Andrew Gusla Nengsih, Yeyi Hidayat, Afifah Naurah Lina Fatimah Lishobrina Lisda Lisda M Yoka Fathoni Maulana, Ihsan Muadin, Dika Alim Muhammad Afrizal Amrustian Nenen Isnaeni Sa'adah, Aminatus Sahara Sahara Sulaeman, Gilang Suprapto, Amelia Rut Utami, Annisaa Yasin, Feri Yohani Setiya Rafika Nur