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All Journal Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK) PENGAMATAN: Jurnal Pengabdian Masyarakat untuk Ilmu MIPA dan Terapannya
Pramono, Doni Eko Hendro
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Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering Mathematics Other

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Menentukan Tingkat Kepuasan Mahasiswa Terhadap Pelayanan Akademik Menggunakan Metode Algoritma K-Means Nanda, Adi Prasetia; Pramono, Doni Eko Hendro; Hartati, Sri
Explore:Jurnal Sistem informasi dan telematika(Telekomunikasi, Multimedia dan Informatika) Vol 11, No 1 (2020): Juni
Publisher : Universitas Bandar Lampung (UBL)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (637.421 KB) | DOI: 10.36448/jsit.v11i1.1455

Abstract

Perbandingan antara metode algoritma C4.5 dengan metode algoritma K-Means yang diteliti oleh peneliti untuk menentukan tingkat kepuasan mahasiswa terhadap pelayanan akademik pada STMIK Pringsewu. Kriteria yang digunakan dalam menilai tingkat kepuasan ada lima kriteria diantara, adalah tangible, reability, assurance, responsiveness, dan empathy. Pada STMIK Pringsewu, kriteria-kriteria tersebut belum terukur dengan pasti oleh karena itu pihak STMIK Pringsewu masih merasa kurang tepat dalam menentukan kriteria mana yang harus ditingkatkan kualitasnya. Menggunakan metode algoritma K-Means penulis mencoba untuk mengukur kelima kriteria tersebut, sehingga terbentuklah sebuah hasil dari kelima kriteria tersebut. Setelah dilakukan perhitungan secara manual, maka dilakukan pula pembuktian menggunakan uji validasi sensitivitas, dan spesifisitas. Setelah dilakukannya perhitungan menggunakan metode algoritma K-Means dan dibuktikan dengan menggunakan uji validasi sensitivitas, dan spesifisitas. Maka didapatkan nilai Uji sensitivitas metode algoritma K-Means dapat mengklarifikasikan kepuasan mahasiswa dengan variable tangible 77,00 %, Reliability 78,83 %, Responsiveness 86,49 %, Assurance 70,07 %, dan Empathy 79,19 %. Sedangkan, uji spesifisitas metode algoritma K-Means dapat 99 mengklarifikasikan tidak puasnya mahasiswa dengan variable tangible 21,35 %, Reliability 21,80 %, Responsiveness 23,72 %, Assurance 19,61 %, dan Empathy 21,89 %
Pengenalan Sinyal Otak Berbasis Machine Learning untuk Aktivasi Lampu Sen Otomatis pada Kendaraan Bermotor (Kasus Ibu-Ibu di Indonesia) Hartantom, Budi; Marliana, Iin; Pramono, Doni Eko Hendro
Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK) 2025: SNESTIK V
Publisher : Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/p.snestik.2025.6901

Abstract

This study proposes an automatic turn signal activation system for motor vehicles based on brain signals using a machine learning approach, with a specific focus on rider behavior, particularly among Indonesian mothers. The system is designed to enhance driving safety by detecting brain signals using EEG devices and processing them through machine learning algorithms to identify the rider's intent to activate the turn signals. Data were collected from various rider groups, processed, and trained using machine learning models to ensure high classification accuracy. The test results indicate that this system effectively recognizes brain signal patterns and automates turn signal activation with adequate accuracy. The implementation of this system is expected to reduce the risk of accidents caused by riders' negligence in providing signals when turning.
Pelatihan Python untuk Sistem Prediksi Hasil Tangkapan Ikan di Pulau Pasaran, Bandarlampung Pramono, Doni Eko Hendro; Sukri, Hamdan; Purnomo, Rosyana Fitria; Hartanto, M Budi
PENGAMATAN: Jurnal Pengabdian Masyarakat untuk Ilmu MIPA dan Terapannya Vol 3 No 1 (2025): PENGAMATAN: Jurnal Pengabdian Masyarakat untuk Ilmu MIPA dan Terapannya
Publisher : Jurusan Matematika FMIPA Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/pengamatanv3i1p27-36

Abstract

The Python training program for a fish catch prediction system in Pasaran Island, Bandarlampung, aims to enhance the efficiency and accuracy of fishery forecasts for local fishermen. Utilizing machine learning algorithms, the system processes environmental data such as sea surface temperature, chlorophyll levels, and weather conditions. This training introduces participants to Python basics, data processing, and the implementation of predictive algorithms like linear regression and artificial neural networks. Results from the training indicate an improvement in participants' understanding of predictive technology, directly supporting decision-making in fisheries activities. Furthermore, the application of this technology is expected to reduce reliance on less precise traditional methods. By integrating spatial and temporal data, this program delivers a prediction system that adapts to changing marine ecosystems, supporting sustainable fishery resource management. The study contributes to the coastal community's capacity to address challenges posed by climate change and marine economic dynamics.
Co-Authors Hartanto, M Budi Hartantom, Budi marliana, iin Nanda, Adi Prasetia Purnomo, Rosyana Fitria Sri Hartati Sukri, Hamdan