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All Journal JUTEI (Jurnal Terapan Teknologi Informasi) Jurnal ULTIMATICS Patria : Jurnal Pengabdian Kepada Masyarakat JOINCS (Journal of Informatics, Network, and Computer Science) JASMED : Journal of Software Engineering and Multimedia
Laurentius Kuncoro Probo Saputra
Universitas Kristen Duta Wacana
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Health Professions Public Health Social Sciences Other

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Pendeteksi Kecelakaan Lalu Lintas Menggunakan Akselerometer dan GPS Location pada Aplikasi Android Marcellinus Julian Finlando; Kristian Adi Nugraha; Laurentius Kuncoro Probo Saputra
Jurnal Terapan Teknologi Informasi Vol 4 No 1 (2020): Jurnal Terapan Teknologi Informasi
Publisher : Fakultas Teknologi Informasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21460/jutei.2020.41.189

Abstract

Kecelakaan kendaraan merupakan hal yang pastinya dihindari semua orang. Kecelakaan kendaraan tidak hanya menyebabkan korban luka-luka, tetapi dapat juga menyebabkan kematian, dan menyebabkan kerugian lain seperti kerugian materi. Dalam kecelakaan kendaraan terdapat peranan penting orang-orang di sekitar, pihak yang berwenang, dan bahkan keluarga untuk menolong korban kecelakaan. Menjadi sebuah permasalahan tersendiri apabila orang-orang tersebut tidak dapat dihubungi oleh korban, dikarenakan korban sudah tidak dapat memegang smartphone karena dalam kondisi yang tidak memungkinkan seperti cidera fisik atau bahkan tidak sadarkan diri. Selain itu terdapat faktor lain seperti orang-orang yang tidak peduli karena tidak tahu ada korban kecelakaan. Dengan memanfaatkan smartphone android yang digunakan oleh sebagian besar masyarakat, maka dapat dirancang dan dibuat sistem deteksi kecelakaan kendaraan pada aplikasi android. Aplikasi tersebut menggunakan sensor akselerometer dan GPS Location sebagai variabelnya dalam mendeteksi kecelakaan. Apabila sistem mendeteksi kecelakaan, maka sistem akan langsung mengirimkan pesan kepada seluruh pengguna aplikasi yang sudah terdaftar. Pesan tersebut berupa lokasi korban kecelakaan, sehingga para pembacanya dapat langsung mengakses lokasi korban via Google Map dari pesan tersebut. Aplikasi ini dapat memudahkan orang-orang di sekitar, kerabat atau pihak yang berwenang untuk dapat mengetahui lokasi kecelakaan kendaraan. Sehingga jika ada kecelakaan kendaraan, maka diharapkan orang-orang yang sudah memiliki dan terdaftar pada aplikasi tersebut dapat menolong korban kecelakaan dan menanganinya secara cepat.
Pemanfaatan Seamless Wireless (EoIP) dan GPS pada Sistem Peringatan Perlintasan Kereta Tanpa Palang Pintu Dio Pramantha; Gani Indriyanta; Laurentius Kuncoro Probo Saputra
Jurnal Terapan Teknologi Informasi Vol 4 No 1 (2020): Jurnal Terapan Teknologi Informasi
Publisher : Fakultas Teknologi Informasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21460/jutei.2020.41.194

Abstract

Perlintasan kereta liar tanpa palang pintu dan tanpa penjaga merupakan awal mula sering terjadinya kecelakaan di perlintasan kereta api. Salah satu solusi untuk menyelesaikan permasalahan ini dengan pembangunan sistem otomatis peringatan kedatangan kereta dengan memanfaatkan teknologi seamless wireless Ethernet over Internet Protocol (EoIP). Penelitian ini dilakukan dengan pengujian prototype di lingkungan UKDW dengan panjang pelintasan kurang lebih 70 - 80 meter, menggunakan 1 MCU(Micro Controller Unit) ESP8266 dan 1 GPS Module yaitu node kereta, 2 MCU, 2 unit LCD module, 2 Servo motor dan 2 Buzzer menjadi 2 node perlintasan kereta. Sistem ini diuji coba sebanyak 30 kali dengan checklist pengujian. Pengujian dimulai saat node kereta mengirimkan informasi dalam bentuk longitude dan latitude ke server lalu server akan menghitung jarak antara node kereta dengan setiap node perlintasan kemudian hasil jarak tersebut akan di kirim ke setiap node perlintasan kereta, akan terjadi pengecekan jarak di setiap node perlintasan kereta, pada jarak yang telah ditentukan palang akan ditutup ataupun dibuka. Dari 30 kali percobaan disimpulkan bahwa sistem otomatis peringatan kedatangan kereta dapat mendeteksi kedatangan kereta dan telah berhasil dibangun dengan benar dan berjalan sesuai flow sistem yang sudah dirancangkan. Dilengkapi dengan pemanfaatan Teknologi seamless wireless EoIP yang memungkinkan pembangunan jaringan bahkan di titik buta tidak dapat dijangkau oleh operator seluler ataupun GSM. Oleh karena itu seamless wireless EoIP ini sangat cocok dalam membantu pembangunan sistem peringatan kedatangan kereta api dengan palang otomatis dibuktikan dengan setiap alat dapat terhubung dan tidak terputus selama 30 kali percobaan.
Webinar dan Workshop Pengenalan Internet of Things (IOT) untuk siswa SMA Kolese De Britto Danny Sebastian; Kristian Adi Nugraha; Laurentius Kuncoro Probo Saputra
Patria : Jurnal Pengabdian Kepada Masyarakat Vol 3, No 2: September 2021
Publisher : Universitas Katolik Soegijapranata

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24167/patria.v3i2.3170

Abstract

Kerjasama antara SMA Kolese De Britto dan Fakultas Teknologi Informasi Universitas Kristen Duta Wacana sudah terjalin dalam beberapa tahun terakhir. Beberapa kegiatan sudah rutin diadakan sebagai bentuk realisasi kerjasama. Saat ini Internet of Things atau IoT berkembang dengan sangat pesat. Beberapa siswa di SMA Kolese De Britto memiliki minat lebih, khususnya pada bidang Internet of Things. Untuk memfasilitasi siswa tersebut, diadakan kegiatan seminar secara daring atau webinar dengan topik Internet of Things. Kegiatan dilaksanakan mulai September 2020. Kegiatan dilanjutkan dengan workshop secara daring untuk siswa yang tertarik mengimplementasikan pengetahuan pada bidang Internet of Things dalam bentuk produk nyata. Kegiatan workshop dilakukan mulai bulan September sampai dengan November 2020. Ada beberapa kendala, seperti masalah akses internet dan alokasi penjadwalan, karena semua acara dilakukan secara online yang juga berbenturan dengan kegiatan sekolah, namun kegiatan workshop dan pelatihan umum dapat terlaksana dengan baik.
Perbandingan Metode Klasifikasi untuk Menentukan Tingkat Kenyamanan Suhu pada Kondisi Rileks Berbasis Sinyal EEG Laurentius Kuncoro Probo Saputra; Ignatia Dhian Estu Karisma Ratri
Ultimatics : Jurnal Teknik Informatika Vol 10 No 2 (2018): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1697.582 KB) | DOI: 10.31937/ti.v10i2.992

Abstract

Temperature control on air conditioner devices is still oriented to the target environment. This control mode ignores one's physiological condition. A person's thermal comfort varies when indoors. Thermal comfort is closely related to environmental thermal satisfaction conditions. EEG signal is a signal that can reflect brain activity. This research objective is provide classifier model for classifiying person’s thermal comfort based on eeg signal. This research used three conditions of room’s temperature. The features used by classfier are avarage frequency band, HFD, PFD, and MSE features. Classifier performance was assessed using ROC curve evaluation. The results of the classification of thermal comfort levels with EEG signals with the KNN classifier are obtained only by using the band frequency average feature, which is equal to 0.878 with a standard deviation of 0.022. While the SVM classifier gets the highest performance by using a combination of the average band + HFD frequency feature, which is 0.877 with a standard deviation of 0.013 in the linear kernel and RBF.
Testing The Accuracy of Fingerprint Recognition using Levenshtein Distance and Hamming Distance Methods Gregorius Sakti Ginantaka; Laurentius Kuncoro Probo Saputra; Sri Suwarno
JOINCS (Journal of Informatics, Network, and Computer Science) Vol 6 No 1 (2023): April
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/joincs.v6i1.1612

Abstract

The presence or evidence of attendance is crucial in monitoring the presence of every individual working in a particular field. Developing an employee attendance system using fingerprints can expedite the processing of data of employees who have or have not attended. One brand of machine used as a fingerprint attendance tool is Fingerspot Flexcode. The data obtained from the machine comes in the form of bitmap images that are converted into strings using encoding. Although the resulting string sequences are different, there is a possibility of similarity in fingerprint data among employees because the system cannot distinguish data precisely. Therefore, the comparison between the Levenshtein Distance and Hamming Distance methods is used to determine which method has the highest accuracy in processing the system's calculation. The method with the highest accuracy will determine the level of compatibility of the method with the tested tool. For example, 6 fingerprint data are taken from each of the 7 different employees, resulting in a total of 42 data as test data. The calculation results show that the accuracy of the Levenshtein Distance method is 80,76 % with a precision of 46,43 %, while the Hamming Distance method is 78,34 % with a precision of 30,50 % in processing string similarity in fingerprint data. Based on these results, it can be concluded that the Levenshtein Distance method is better in calculating similarity in fingerprint data compared to the Hamming Distance method because it has a higher level of accuracy and precision compared to the Hamming Distance method.
Implementation of Internet of Things (IoT) in Smart Medicine Box for the Elderly Yeremia Yudha Setia Graha Susanto; Restyandito; Laurentius Kuncoro Probo Saputra
JOINCS (Journal of Informatics, Network, and Computer Science) Vol 6 No 1 (2023): April
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/joincs.v6i1.1614

Abstract

Consuming medicine on time is important for people who are sick. Medicine that prescribed by a doctor or pharmacist have various kind of rules, for example some medicine must be taken 3 times a day, 2 times a day, and 1 time a day, then there are rules after eating or before eating. Patients treated in the hospital are supervised by nurses, doctors, and receive more supervision from the hospital, but what if the patient is outpatient and the patient is an elderly person. With the presence of IoT technology that is not too expensive, a smart medicine box was made to be used to remind outpatients to take medicine which implements IoT technology and the android application used to manage the pillbox. The medicine box can remind patients to take medicine with the alarm module that is in the smart medicine box. System Usability Scale (SUS) is used to measure the success of the application. Smart medicine box is tested directly with data created to test the existing system. The result of testing the smart medicine box with existing data show that for each existing case, the smart medicine box system runs as it should. Application test results from all respondents received an average score of SUS 73,75. The score results show that the application can be accepted, get a grade scale worth B, and get good grades.
Prediksi Penawaran Mata Kuliah Studi Kasus Prodi Informatika Universitas Kristen Duta Wacana Brian Bastian; Maria Nila Anggia Rini; Laurentius Kuncoro Probo Saputra
JASMED : Journal of Software Engineering and Multimedia Vol 1 No 1 (2023): JASMED : Journal of Software Engineering and Multimedia
Publisher : Institut Teknologi Telkom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/jasmed.v1i1.1118

Abstract

For taking a course, students are required to make a schedule so that the courses to be taken can be according to their choice. There are several obstacles when taking courses, for example when the number of students taking a course is more than the capacity estimated by the campus, so that the classes that are opened are not sufficient. Therefore, a prediction system is needed that can display how many classes are recommended to be opened and the number of students who are likely to take those classes. In the process of making the prediction system, the first thing to do is to determine what features will be used. These features are then applied to all courses and see what combinations occur. Next is to create an algorithm based on these features in the PHP programming language. Prior to that, data from any table in the database was determined beforehand to be processed so that the system could run properly and accurately. The next step is to make input available to users who can determine the minimum number of repeat students, class capacity, lab capacity, and predicted semester. The results of the research conducted and the system created allow it to predict courses based on the features that have been entered. The data used in the database is also the most recent, so the amount issued to the prediction system is an accurate amount. This system can be said to be successful because it can display recommended courses along with the number of students who are repeating and students who have not taken those courses.
Co-Authors Brian Bastian Danny Sebastian Dio Pramantha Gani Indriyanta Gregorius Sakti Ginantaka Ignatia Dhian Estu Karisma Ratri Kristian Adi Nugraha Marcellinus Julian Finlando Restyandito Rini, Maria Nila Anggia Sri Suwarno Yeremia Yudha Setia Graha Susanto