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All Journal Jurnal MIPA Journal of Engineering, Computer Science and Information Technology (JECSIT)
Afrioni Roma Rio
Universitas Sam Ratulangi
Biochemistry, Genetics & Molecular Biology Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Mathematics Physics

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PREDIKSI JARAK TEMPUH KAPAL MOTOR SANGIANG MENGGUNAKAN SUPERVISED MACHINE LEARNING Afrioni Roma Rio; Berton Maruli Siahaan
Journal of Engineering, Computer Science and Information Technology (JECSIT) Vol 2, No 2 (2022): Volume 2 Nomor 2, Oktober 2022
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jecsit.v2i2.249

Abstract

As a maritime nation with thousands of islands and a vast sea area, sea transportation is the most effective transportation used by the people of Indonesia. A motorboat is one type of maritime transportation that is used to move people or commodities. In this article, we will discuss predicting the daily mileage of one of the motorboats, the Sangiang, which travels from Bitung to Ternate. Three independent variables, Anchor Time (minutes), Speed (knots/hour), and Sailing Time (minutes), are used in supervised machine learning techniques to estimate the daily mileage (mile). Of the various methods evaluated, the multiple regression model was found to be the most accurate at forecasting the Sangiang motorboat’s daily mileage.
Pengaruh Miopi Terhadap Aktivitas Gelombang Alfa pada Otak di Area Oksipital Ernawatil Gani; Mahendra Kusuma Nugraha; Afrioni Roma Rio
Jurnal MIPA Vol. 13 No. 2 (2024): Cover
Publisher : Universitas Sam Ratulangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35799/jm.v13i2.56125

Abstract

Miopi merupakan kondisi mata seseorang yang mengalami kesulitan dalam mengamati objek yang jauh dengan jelas. Kondisi ini akan mempengaruhi kerja sistem saraf di otak. Sistem saraf di otak dapat diketahui melalui beberapa aktivitas gelombang salah satunya gelombang alfa. Gelombang ini berhubngan dengan kemampuan mata memvisualisasikan suatu benda. Sehingga, penelitian ini bertujuan untuk menganalisis kondisi sinyal alfa di otak khususnya area oksipital pada penderita miopi menggunakan metode Power Spectral Density (PSD). Sebanyak tujuh peserta direkam menggunakan EEG Emotiv Epoc masing-masing selama 5 menit dengan mata terbuka tanpa kacamata dan dengan kacamata. Data EEG diolah melalui filtering, rejecting artefak, dan analisis PSD dengan metode Periodogram Welch menggunakan Python. Hasil menunjukkan perbedaan signifikan dalam distribusi daya frekuensi antara area oksipital pada otak bagian kiri (O1) dan bagian kanan (O2), dengan aktivitas alfa yang lebih dominan dan konsisten di O2 pada frekuensi 10-11 Hz. Dengan demikian, kondisi ini mengindikasikan aktivitas neural yang lebih intens di oksipital kanan pada penderita miopi, mungkin sebagai bentuk kompensasi atau adaptasi dalam pemrosesan visual. Analisis lebih lanjut diperlukan untuk memahami implikasi penuh dari aktivitas neural ini dan untuk memastikan hasil yang lebih valid melalui teknik pengolahan sinyal yang lebih canggih. Myopia is a condition in which a person's eyes have difficulty seeing distant objects clearly, affecting the neural system's function in the brain. The nervous system of the brain can be identified through several wave activities, including alpha waves, which are related to the eye's ability to visualize objects. Therefore, this study aims to analyze the condition of alpha signals in the brain, specifically in the occipital area, of individuals with myopia using the Power Spectral Density (PSD) method. Seven participants were recorded using the EEG Emotiv Epoc for 5 minutes each with eyes open, both with and without glasses. The EEG data was processed through filtering, artifact rejection, and PSD analysis using the Welch Periodogram method in Python. The results showed significant differences in the frequency power distribution between the occipital on the left side of the brain (O1) and on the right side (O2), with more dominant and consistent alpha activity in O2 at 10-11 Hz. This indicates more intense neural activity in the right occipital region in myopic individuals, possibly as a form of compensation or adaptation in visual processing. Further analysis is required to fully understand the implications of this neural activity and to ensure more valid results through advanced signal processing techniques.
Pemetaan Potensi Energi Matahari di Sulawesi Utara menggunakan Machine Learning K-Means Afrioni Roma Rio; Berton Maruli Siahaan; Ernawatil Gani
Jurnal MIPA Vol. 14 No. 1 (2024): Artikel
Publisher : Universitas Sam Ratulangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35799/jm.v14i2.57778

Abstract

Penelitian ini mengkaji potensi energi matahari di Sulawesi Utara dengan menganalisis parameter lingkungan seperti suhu, kelembaban relatif, jumlah awan, dan radiasi matahari selama periode 2018 hingga 2022. Metode machine learning K-Means digunakan untuk mengelompokkan data secara optimal, dengan penentuan jumlah klaster terbaik melalui metode siku. Penggunaan machine learning ini penting untuk menangani data yang besar dan kompleks, serta mengidentifikasi pola tersembunyi yang membantu pemetaan potensi energi matahari. Hasil analisis menunjukkan bahwa Klaster 2, yang terdiri dari wilayah dengan suhu tinggi dan radiasi matahari yang optimal, memiliki potensi terbesar untuk instalasi tenaga surya skala besar, didukung oleh infrastruktur tenaga surya yang sudah ada di wilayah pada klaster tersebut. Penelitian ini menghasilkan peta energi surya hingga tingkat desa, yang dapat digunakan untuk pengembangan energi surya di Sulawesi Utara This study examines the solar energy potential in North Sulawesi by analyzing environmental parameters such as temperature, relative humidity, cloud cover, and solar irradiance over the period of 2018 to 2022. The machine learning K-means method was used to optimally cluster the data, with the best number of clusters determined through the elbow method. The use of machine learning is important for handling large and complex datasets, as well as identifying hidden patterns that aid in mapping solar energy potential. The analysis results show that Cluster 2, which consists of areas with high temperatures and optimal solar irradiance, has the greatest potential for large-scale solar power installations, supported by existing solar infrastructure in the region. This study produces a detailed solar energy map down to the village level, which can be used for the development of solar energy in North Sulawesi  
Co-Authors Berton Maruli Siahaan Ernawatil Gani Mahendra Kusuma Nugraha