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All Journal Jurnal Transportasi Scientific Journal of Informatics Journal of Natural Sciences and Mathematics Research Warta Penelitian Perhubungan PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat JURNAL IT: Media Infromasi STMIK Handayani Makassar Jurnal Teknologi Informasi, Komputer, dan Aplikasinya (JTIKA ) Indonesian Journal of Statistics and Its Applications Warta Penelitian Perhubungan
Armin Lawi
Universitas Hasanuddin
Agriculture, Biological Sciences & Forestry Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Physics Public Health Social Sciences Transportation Other

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Kajian Waktu Tempuh Perencanaan Penumpang Dan Bagasi di Terminal Kedatangan Bandar Udara Internasional Sultan Hasanudin Makassar Sabur, Fatmawati; Jinca, M. Yamin; Lawi, Armin
Warta Penelitian Perhubungan Vol. 25 No. 1 (2013): Warta Penelitian Perhubungan
Publisher : Sekretariat Badan Penelitian dan Pengembangan Perhubungan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25104/warlit.v25i1.703

Abstract

Bandara mengatur pergerakan barang dan orang melalui saluran udara. Layanan bagasi merupakan bagian dari Jasa Ground Handling yang diangkut oleh pesawat, baik untuk berangkat atau tiba. Salah satu kegiatan di bandara adalah penanganan bagasi Anda (bagasi) yang dibawa oleh penumpang. Pada beberapa waktu tertentu, terutama dalam penerbangan solid state, seorang penumpang di Bandara Internasional Sultan Hasanuddin Makassar masih mengalami keterlambatan penerimaan bagasi di terminal kedatangan. Tujuan penelitian, untuk mengetahui perbedaan perjalanan waktu dan faktor-faktor yang mempengaruhi perbedaan waktu tempuh dalam pergerakan penumpang dan bagasi. Jenis penelitian adalah studi korelasi, dengan menggunakan data primer berupa pengukuran data lapangan langsung serta data sekunder dari laporan-MATSC bulanan Operasi Dukungan Layanan Divisi lnformasi Aeronautical dalam bentuk jadwal penerbangan tetap dan Side Air Divisi Operasi ( Momen Apron Control-AMC) dalam bentuk apron pergerakan data. Pengolahan data menggunakan SPSS versi 18 Dengan variabel data: JumlahGround (Xl),JumlahPeralatanGround (X2), jarak ke Gerakan Penumpang Kedatangan (X3), jarak ke Gerakan Bagasi Kedatangan (X4) dan waktu petjalanan (Y), metode / akses ke pergerakan penumpang dan bagasi untuk kedatangan melalui tiga cara: garbarata / aviobridge, Bus dan Walk. Temuan dari penelitian ini adalah nilai besamya variabel ~I X2 I X3 and X4 perubahan y diproses nilai ANOV A signifikan diperoleh dengan gerakan akses aviobridge, bus dan berjalan kaki, masingmasing sebesar 0,000 dengan derajat kepercayaan 95% atau a = 0, 05 yang berarti bahwa keempat variabel independen (~ , ~ , ~ and X4) memiliki pengaruh yang signifikan terhadap variabel Y (waktu). Duncan hasil tes dapat dilihat perbedaan waktu rata-rata antara pergerakan penumpang dan bagasi menggunakan 3 metode / gerakan akses yaitu: perbedaan waktu terkecil antara penumpang dan bagasi terjadi ketika menggunakan bus, diikuti garbarata dan perbedaan yang besar dalam waktu saat berjalan, sehingga dapat disimpulkan bahwa penggunaan bus lebih efektif dalam mengurangi keterlambatan penerimaan bagasi distasiunkedatangan Bandara Internasional Sultan Hasanuddin Makassar.
Application of Adaptive Synthetic Nominal and Extreme Gradient Boosting Methods in Determining Factors Affecting Obesity: A Case Study of Indonesian Basic Health Research Survey 2013: Aplikasi Metode Adaptive Synthetic Nominal dan Extreme Gradient Boosting dalam Menentukan Faktor yang Memengaruhi Obesitas: Studi Kasus Riset Kesehatan Dasar Indonesia 2013 Rombe, Yoris; Thamrin, Sri Astuti; Lawi, Armin
Indonesian Journal of Statistics and Applications Vol 6 No 2 (2022)
Publisher : Statistics and Data Science Program Study, IPB University, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v6i2p309-317

Abstract

Obesity is the accumulation of excessive body fat and can be harmful to health. According to recent studies, several factors that contribute to the increasing prevalence of obesity in Indonesia include poor diet, lack of consumption of vegetables and fruits, high consumption of fast food, area of residence, and lack of physical activity. In addition, psychological factors, high consumption of alcohol and cigarettes, cultural differences, and stress factors also trigger obesity. The rapid development of the medical field cannot be separated from the availability of data that is increasingly easy to access and increasing knowledge in the medical field. This makes machine learning increasingly needed for pattern recognition from very large medical data, including obesity data. In this study, the factors that influence obesity status in Indonesia will be determined. In order to achieve this, Extreme Gradient Boosting (XGBoost) was used. This method is one of the classification methods that has better scalability and more efficient over its previous methods. Besides that, to overcome the imbalanced data, Adaptive Synthetic Nominal Algorithm (ADASYN-N) is used in order to balance the data and improve its prediction accuracy. Both the ADASYN-N and XGBoost methods will be applied to obesity data from the Indonesian Basic Health Research Survey in 2013. This study shows that female is more at risk in determining obesity status in Indonesia based on the highest gain value (37%). In addition, age 35-54 years, strenuous activity, and eating vegetables for 6 days are also risk factors of obesity.
Early detection model of Parkinson's Disease using Random Forest Method on voice frequency data Rifqah Fahira, Nurul; Lawi, Armin; Aqsha, Masjidil
Journal of Natural Sciences and Mathematics Research Vol. 9 No. 1 (2023): June
Publisher : Faculty of Science and Technology, Universitas Islam Negeri Walisongo Semarang

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

Abstract

Parkinson's disease is the most common nervous system disease that affects all ethnicities, genders, and ages, with a higher prevalence in the elderly and men. Developing countries tend to have higher cases of Parkinson's. The prevalence of death due to Parkinson's in Indonesia reaches the fifth highest cases in Asia and 12th in the world. This neurodegenerative disease affects a person's ability to control movement. Currently, the diagnosis of Parkinson's disease is only based on observation of motor symptoms. Therefore, early detection of the disease cannot be done. His paper proposes an efficient way to detect Parkinson's disease symptoms by comparing the fundamental frequencies of patients' voices using the random forest method. Random forest is a Machine Learning method that applies the ensemble concept, which aims to improve the performance of the classification by combining several decision trees as a basis. Random forests have shown superior algorithm performance in numerous health studies. In this study, the dataset consisted of 20 patients with Parkinson's and 20 normal patients. Data for each patient was taken from 26 types of voice records, and thus, the total data was 1,040 observations. The obtained data is prepared by filtering and rescaling. Then, the data is split and modelled using the Random Forest Method. The random forest model obtained accuracy results of 72.50%, precision (normal) of 72.28%, precision (Parkinson's) of 72.73%, sensitivity (normal) of 73.00%, sensitivity (Parkinson's) of 72.00% and AUC is 80.70%. The built random forest model is quite good at Parkinson's disease detection.
Co-Authors ', Wardi Amal, Muhammad Ikhlasul Andani Achmad Andi Ilham Latunra, Andi Ilham Anggitamaya, Anggitamaya Aqsha, Masjidil Eliyah Acantha Manapa Sampetoding Ery Murniyasih Faisal, Faradias Izza Azzahra Fajri S, Ahmad Fatmawati Sabur Gunawan Gunawan M. Yamin Jinca Miftahulkhairah, Miftahulkhairah Raden Mohamad Herdian Bhakti Rifqah Fahira, Nurul Rombe, Yoris S, Mulyadi Salsabila, Unik Hanifah Sir, Muhammad Mochsen Sri Astuti Thamrin Wahyuni Ekasasmita Yamin Jinca