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All Journal English Language and Literature Microbiology Indonesia Jurnal Online Mahasiswa (JOM) Bidang Ilmu Ekonomi Jurnal Online Mahasiswa (JOM) Bidang Keguruan dan Ilmu Pendidikan Pedagogik : Jurnal Ilmiah Pendidikan dan Pembelajaran Fakultas Tarbiyah Unversitas Muhammadiyah Aceh MaPan : Jurnal Matematika dan Pembelajaran Manutech : Jurnal Teknologi Manufaktur MEANS (Media Informasi Analisa dan Sistem) Jurnal Teknologi Informasi Jurnal Pendidikan Indonesia (Japendi) Jurnal Ilmu dan Inovasi Fisika ABDIMU: Jurnal Pengabdian Muhammadiyah
Muhammad Fadhil Z, Rusdi Noor Rosa (p:43-50)
Unknown Affiliation
Religion Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Environmental Science Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Social Sciences Other

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Rancang Bangun Pengukur Persentase Lemak Tubuh, Suhu Tubuh, dan Kadar Oksigen dalam Darah Berbasis IoT Fadhillah, Muhammad; Kullaha, Razila; Sulistyo, Eko; Silalahi, Parulian
Manutech : Jurnal Teknologi Manufaktur Vol. 16 No. 02 (2024): Manutech: Jurnal Teknologi Manufaktur
Publisher : Politeknik Manufaktur Negeri Bangka Belitung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33504/manutech.v16i02.358

Abstract

  This research aims to design a multifunctional device based on the Internet of Things (IoT) that utilizes Bioelectrical Impedance Analysis (BIA) method to simultaneously measure body fat percentage, body temperature, and blood oxygen levels. The device is equipped with a bioimpedance sensor to measure body fat percentage, as well as integrated temperature and oxygen sensors to detect body temperature and blood oxygen levels. Testing the device yielded highly satisfactory results with a high level of accuracy, including weight at 99.32%, height at 99.83%, body fat percentage at 97.96%, non-fat mass at 97.45%, total body water at 99.86%, body temperature at 98.61%, and oxygen levels at 99.83%. Measurement results are displayed on an LCD screen and transmitted to the Telegram application through the IoT platform. By integrating BIA, IoT, and multi-functional sensors, it is anticipated that this device can provide accurate and integrated measurements, contributing to more effective health monitoring.
SISTEM PENDUKUNG KEPUTUSAN PENILAIAN KEPUASAN PELANGGAN PADA BENGKEL BUYUNG METODE AHP BERBASIS JAVA Fadhillah, Muhammad
Jurnal Teknologi Informasi Vol 3, No 2 (2025): Februari 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat - Universitas Teknologi Digital Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/juti.v3i2.1001

Abstract

Penelitian ini bertujuan untuk mengimplementasikan Sistem Pendukung Keputusan (SPK) berbasis metode Analytical Hierarchy Process (AHP) dalam mengukur tingkat kepuasan pelanggan di Bengekel Buyung. Dalam rangka meningkatkan kualitas pelayanan dan memahami preferensi pelanggan, studi ini memberikan gambaran tentang kepuasan pelanggan melalui pendekatan sistematis yang melibatkan berbagai kriteria dan faktor. Latar belakang penelitian mencakup kompleksitas dalam mengidentifikasi kebutuhan pelanggan dan memahami ekspektasi mereka. Metode AHP digunakan untuk membangun struktur hirarki dan melakukan perbandingan berjenjang terhadap kriteria yang relevan untuk menghasilkan penilaian relatif terhadap kepuasan pelanggan. Hasil penelitian ini diharapkan dapat memberikan panduan bagi manajemen Bengekel Buyung dalam mengidentifikasi area peningkatan layanan, menciptakan pengalaman positif bagi pelanggan, serta meningkatkan kesetiaan pelanggan dalam jangka Panjang.
EDUCATION TO MAINTAIN FRIENDSHIP TO STRENGTHEN THE FOUNDATIONS OF PEACE IN SOCIETY Fadhillah, Muhammad; Emawati, Emawati; Sulastri, Ema; Yusliani, Hamdi; Rosnidarwati, Rosnidarwati
ABDIMU: Jurnal Pengabdian Muhammadiyah Vol 4, No 1 (2024): Vol 4, No 1 Juni 2024
Publisher : Universitas Muhammadiyah Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37598/abdimu.v4i1.2101

Abstract

According to Islamic teachings, human beings are connected to each other, and as such, they naturally require the company of other people. Due to their inherent weakness, humans depend on the support of people around them. Thus, it is not unusual to refer to humans as social beings. This is owing to the fact that humans are beings who will always require the company of other people. This suggests that all Muslims have a duty to uphold friendship, whether it is through familial ties (descendants) or brotherly relationships with other Muslims. It is necessary for non-Muslims to uphold the virtue of mutual respect and appreciation, even if they go about it in various ways. It is hard to establish harmony and peace within a society if its people lack affection, as this always results in arguments, animosity, and ultimately, murder. For this reason, shilaturrahim (engaging with others)—both general and specific—are essential to bringing about the world's peace, harmony, and unification of humanity. Keywords: Society, Engagement with others, peace
Analisis dan Penerapan Algoritma Naïve Bayes untuk Klasifikasi Penyakit Gingivitis Fadhillah, Muhammad; Sari, Herlina Latipa; Elfianty, Lena
MEANS (Media Informasi Analisa dan Sistem) Volume 8 Nomor 2
Publisher : LPPM UNIKA Santo Thomas Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54367/means.v8i2.3264

Abstract

The aim of this research is to implement the Naïve Bayes algorithm for gingivitis classification at Dental Polyclinic, UPTD Pasar Ikan Health Center Bengkulu. The diagnosis of gingivitis is one of the diagnoses with severe conditions that are often complained at Dental Polyclinic, UPTD Pasar Ikan Health Center Bengkulu. However, the problem is that the services are still very limited because doctors are only on duty for 2 days a week. Therefore, a method is needed that is able to classify the risk level of various gingivitis diagnoses that occur at UPTD Pasar Ikan Health Center Bengkulu so that it can be immediately treated with appropriate action using Naïve Bayes method. From the results of tests carried out by Naïve Bayes method, it can be used as a solution for using this system. In its implementation, Naïve Bayes method can classify types of gingivitis at UPTD Pasar Ikan Health Center Bengkulu
Advancing Aviation Meteorology: Airport Visibility Prediction Using Random Forest Regressor On Integrated Metar Parameters Kharisma, Adilaksa; Fadhillah, Muhammad; Haryanto, Yosafat Donni
JIIF (Jurnal Ilmu dan Inovasi Fisika) Vol 9, No 2 (2025)
Publisher : Universitas Padjadjaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24198/jiif.v9i2.65464

Abstract

To provide accurate and reliable visibility information in support of aviation safety at Soekarno-Hatta International Airport, a visibility prediction system was developed using the Random Forest Regressor algorithm based on 2024 METAR data. Visibility is a critical parameter for flight safety, particularly under adverse weather conditions. The dataset includes wind direction and speed, temperature, dew point, air pressure, weather phenomena, and cloud parameters that were numerically encoded. After preprocessing and quality control, the data was input into a Random Forest model optimized using Grid Search. Evaluation results show strong predictive performance with an R² value of 0.8736, MAE of 607.45 m, and RMSE of 772.29 m. Feature importance analysis identified haze, temperature, and mist as the most influential factors affecting visibility. These findings demonstrate that integrating meteorological observational data with machine learning approaches can provide accurate visibility predictions to support aviation operational decision-making.
Analisis Prediksi Parameter Signifikansi Siklon Tropis di Wilayah Utara dan Selatan Indonesia Fadhillah, Muhammad; Gilbert, Kevin; Saputro, Ogi Rahmawan Adi
Jurnal Pendidikan Indonesia Vol. 6 No. 3 (2025): Jurnal Pendidikan Indonesia
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/japendi.v6i3.7284

Abstract

Tropical cyclones are atmospheric phenomena that occur in warm ocean areas, including the northern and southern regions of Indonesia. Although rarely formed directly in Indonesia, tropical cyclones from the Pacific and Indian Oceans often have a significant impact on weather conditions in Indonesia. This study was conducted to identify the influence of Sea Surface Temperature (SST) on the formation and development of tropical cyclones using a machine learning approach with a Random Forest model. The data used was in the form of reanalysis of SST data sourced from ECMWF (European Center for Medium - Range Weather Forecast) and analyzed for six tropical cyclone events representing the northern and southern regions of Indonesia. In addition, this study also aims to evaluate the effectiveness of machine learning-based prediction models in predicting SST parameters by using evaluation metrics such as RMSE, MAE, and R² to ensure prediction accuracy. The results showed that the SST values that supported the formation of tropical cyclones ranged from 31–33°C, which corresponds to the minimum temperature criteria for the formation of tropical cyclone systems. The Random Forest model showed excellent performance with low RMSE and MAE scores, and an R² value close to 1 in all cases tested with Tropical Cyclone Dahlia being the best case with the highest prediction accuracy. This study shows that the Random Forest model is able to effectively capture complex patterns of SST and provide accurate predictions, potentially as an instrument to understand and mitigate risks associated with tropical cyclone events in Indonesia.
Advancing Aviation Meteorology Airport Visibility Prediction Using Random Forest Regressor on Integrated METAR Parameters Kharisma, Adilaksa; Fadhillah, Muhammad; Haryanto, Yosafat Donni
JIIF (Jurnal Ilmu dan Inovasi Fisika) Vol 9, No 2 (2025)
Publisher : Universitas Padjadjaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24198/jiif.v9i2.65479

Abstract

To provide accurate and reliable visibility information in support of aviation safety at Soekarno-Hatta International Airport, a visibility prediction system was developed using the Random Forest Regressor algorithm based on 2024 METAR data. Visibility is a critical parameter for flight safety, particularly under adverse weather conditions. The dataset includes wind direction and speed, temperature, dew point, air pressure, weather phenomena, and cloud parameters that were numerically encoded. After preprocessing and quality control, the data was input into a Random Forest model optimized using Grid Search. Evaluation results show strong predictive performance with an R² value of 0.8736, MAE of 607.45 m, and RMSE of 772.29 m. Feature importance analysis identified haze, temperature, and mist as the most influential factors affecting visibility. These findings demonstrate that integrating meteorological observational data with machine learning approaches can provide accurate visibility predictions to support aviation operational decision making.
Co-Authors ', Azhar ', Marnis ', Marzolina Adrian, Muhammad Buhairi, Fathan Eko Sulistyo Ema Sulastri Emawati emawati Fakhruddin Z Gilbert, Kevin Hamdi Yusliani Haryanto, Yosafat Donni Herlina Latipa Sari Kharisma, Adilaksa KHOMAINI HASAN Kullaha, Razila Lena Elfianty Mulyasir, Mulyasir OGI BUDIANTORO Parulian Silalahi Rosnidarwati, Rosnidarwati Saputro, Ogi Rahmawan Adi SOETIJOSO SOEMITRO SUTARYA ENUS Toto Subroto Wulan Pertiwi