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All Journal Jurnal Informasi dan Teknologi Leuser Journal of Environmental Studies Journal of Educational Management and Learning Infolitika Journal of Data Science
Razief Perucha Fauzie Afidh
Department Of Informatics, Faculty Of Mathematics And Natural Sciences, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia
Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Mathematics Other

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Exploring Geothermal Manifestations in Ie Jue, Indonesia: Enhancing Safety with Unmanned Aerial Vehicle Aprianto, Aprianto; Maulana, Aga; Noviandy, Teuku Rizky; Lala, Andi; Yusuf, Muhammad; Marwan, Marwan; Afidh, Razief Perucha Fauzie; Irvanizam, Irvanizam; Nizamuddin, Nizamuddin; Idroes, Ghazi Mauer
Leuser Journal of Environmental Studies Vol. 1 No. 2 (2023): November 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ljes.v1i2.75

Abstract

Geothermal energy is a renewable resource derived from the Earth's interior that provides an environmentally friendly alternative. Indonesia is at the forefront of geothermal potential, possessing ample resources primarily concentrated in places like Sumatra. However, there is a requirement for greater exploitation of this potential. This research utilizes unmanned aerial vehicles (UAVs) and thermal imaging to detect geothermal indications in the Ie Jue region of Sumatra within the province of Aceh, Indonesia. The analysis focuses on three main manifestation locations using FLIR One thermal camera and water temperature gauges. The study leverages satellite imagery for comparative purposes. Temperature data highlights variations among distinct manifestations, underscoring the necessity for thorough exploration. Moreover, the study devises a secure pathway for researchers to access the site. This investigation contributes to comprehending geothermal activity and its possible role in sustainable energy and other domains.
Digital Transformations in Vocational High School: A Case Study of Management Information System Implementation in Banda Aceh, Indonesia Idroes, Rinaldi; Subianto, Muhammad; Zahriah, Zahriah; Afidh, Razief Perucha Fauzie; Irvanizam, Irvanizam; Noviandy, Teuku Rizky; Sugara, Dimas Rendy; Mursyida, Waliam; Zhilalmuhana, Teuku; Idroes, Ghalieb Mutig; Maulana, Aga; Nurleila, Nurleila; Sufriani, Sufriani
Journal of Educational Management and Learning Vol. 1 No. 2 (2023): December 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/jeml.v1i2.128

Abstract

This study examines the digital transformation in vocational education through the implementation of a Management Information System (MIS) in Banda Aceh, Indonesia. Focused on enhancing educational administration and decision-making, the study provides insightful analysis on the integration of MIS in State Vocational High School (SMK), specifically SMKN 1 and SMKN 3 in Banda Aceh. A purposive sampling method was employed for usability testing. The questionnaire-based usability test revealed high reliability and positive user responses across multiple indicators. Data analysis affirmed the system's high user satisfaction, effectiveness, and ease of use. Despite limitations, the study highlights the significant potential of well-designed MIS in improving operational efficiency and user satisfaction in educational settings. Future research directions include expanding the sample size, conducting longitudinal studies, incorporating qualitative methods, and exploring the impact on educational outcomes, to enhance the generalizability and depth of understanding regarding the role of MIS in education.
ANFIS-Based QSRR Modelling for Kovats Retention Index Prediction in Gas Chromatography Idroes, Rinaldi; Noviandy, Teuku Rizky; Maulana, Aga; Suhendra, Rivansyah; Sasmita, Novi Reandy; Muslem, Muslem; Idroes, Ghazi Mauer; Jannah, Raudhatul; Afidh, Razief Perucha Fauzie; Irvanizam, Irvanizam
Infolitika Journal of Data Science Vol. 1 No. 1 (2023): September 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijds.v1i1.73

Abstract

This study aims to evaluate the implementation and effectiveness of the Adaptive Neuro-Fuzzy Inference System (ANFIS) based Quantitative Structure Retention Relationship (QSRR) to predict the Kovats retention index of compounds in gas chromatography. The model was trained using 340 essential oil compounds and their molecular descriptors. The evaluation of the ANFIS models revealed promising results, achieving an R2 of 0.974, an RMSE of 48.12, and an MAPE of 3.3% on the testing set. These findings highlight the ANFIS approach as remarkably accurate in its predictive capacity for determining the Kovats retention index in the context of gas chromatography. This study provides valuable perspectives on the efficiency of retention index prediction through ANFIS-based QSRR methods and the potential practicality in compound analysis and chromatographic optimization.
Predicting Obesity Levels with High Accuracy: Insights from a CatBoost Machine Learning Model Maulana, Aga; Afidh, Razief Perucha Fauzie; Maulydia, Nur Balqis; Idroes, Ghazi Mauer; Rahimah, Souvia
Infolitika Journal of Data Science Vol. 2 No. 1 (2024): May 2024
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijds.v2i1.195

Abstract

This study aims to develop a machine learning model using the CatBoost algorithm to predict obesity based on demographic, lifestyle, and health-related features and compare its performance with other machine learning algorithms. The dataset used in this study, containing information on 2,111 individuals from Mexico, Peru, and Colombia, was used to train and evaluate the CatBoost model. The dataset included gender, age, height, weight, eating habits, physical activity levels, and family history of obesity. The model's performance was assessed using accuracy, precision, recall, and F1-score and compared to logistic regression, K-nearest neighbors (KNN), random forest, and naive Bayes algorithms. Feature importance analysis was conducted to identify the most influential factors in predicting obesity levels. The results indicate that the CatBoost model achieved the highest accuracy at 95.98%, surpassing other models. Furthermore, the CatBoost model demonstrated superior precision (96.08%), recall (95.98%), and F1-score (96.00%). The confusion matrix revealed that the model accurately predicted the majority of instances in each obesity level category. Feature importance analysis identified weight, height, and gender as the most influential factors in predicting obesity levels, followed by dietary habits, physical activity, and family history of overweight. The model's high accuracy, precision, recall, and F1-score and ability to handle categorical variables effectively make it a valuable tool for obesity risk assessment and classification. The insights gained from the feature importance analysis can guide the development of targeted obesity prevention and management strategies, focusing on modifiable risk factors such as diet and physical activity. While further validation on diverse populations is necessary, the CatBoost model's results demonstrate its potential to support clinical decision-making and inform public health initiatives in the fight against the global obesity epidemic.
Pemodelan Topik Menggunakan n-Gram dan Non-negative Matrix Factorization Afidh, Razief Perucha Fauzie; Syahrial
Jurnal Informasi dan Teknologi 2023, Vol. 5, No. 1
Publisher : SEULANGA SYSTEM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60083/jidt.v5i1.385

Abstract

Pemodelan topik merupakan teknik pembelajaran mesin yang digunakan untuk melihat topik dalam sekumpulan dokumen teks. Pada penelitian ini pemodelan topik yang digunakan adalah Non-Negative Matrix Factorization (NMF) dengan n-gram. Preprocessing seperti penghilangan tanda baca, angka dan stopword diimplementasikan pada penelitian ini. Proses ini dilakukan dengan terlebih dahulu mengubah kata yang terdapat dalam artikel menjadi kata berhuruf kecil. Penelitian ini juga mengeksplorasi keefektifan penerapan unigram, bigram, dan trigram pada pemodelan topik. Pada penelitian ini juga menggunakan coherence value untuk menentukan jumlah topik terbaik yang dapat dibentuk. Data yang digunakan pada penelitian ini berjumlah 53.920 artikel berita yang bersumber dari portal berita RMOL.id dan BeritaSatu.com untuk periode Juli sampai Desember 2022. Visualisasi t-SNE digunakan untuk melihat distribusi pembentukan topik. Berdasarkan hasil penelitian yang dilakukan diperoleh bahwa jumlah topik yang dapat dibentuk dari RMOL.id untuk unigram adalah 15 topik dengan nilai coherence value 0.812748, bigram adalah 10 topik dengan nilai coherence value 0.835738 dan trigram adalah 7 topik dengan nilai coherence value 0.830572. Sedangkan pada BeritaSatu.com diperoleh 10 topik untuk unigram dengan nilai coherence value 0.799718, bigram 15 topik dengan nilai coherence value 0.788762 dan trigram 15 topik dengan nilai coherence value 0.801935.
Co-Authors Aprianto . Ghazi Mauer Idroes Idroes, Ghalieb Mutig Irvanizam, Irvanizam Lala, Andi Marwan Marwan Maulana, Aga Maulydia, Nur Balqis Muhammad Subianto Muhammad Yusuf Mursyida, Waliam Muslem Muslem, Muslem Nizamuddin Nizamuddin Nurleila, Nurleila Raudhatul Jannah Rinaldi Idroes Sasmita, Novi Reandy Souvia Rahimah sufriani, sufriani Sugara, Dimas Rendy Suhendra, Rivansyah Syahrial Teuku Rizky Noviandy Zahriah, Zahriah Zhilalmuhana, Teuku