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All Journal Jurnal TIMES SMARTICS Journal Jurnal Teknik Informatika UNIKA Santo Thomas Jurnal ICT : Information Communication & Technology KAKIFIKOM : Kumpulan Artikel Karya Ilmiah Fakultas Ilmu Komputer Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) Ulead : Jurnal E-pengabdian
Silitonga, Parasian DP
Universitas Katolik Santo Thomas
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Other

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Journal : Jurnal ICT : Information Communication

 1 
Perbandingan Algoritma k-Nearest Neighbors (k-NN) dan Support Vector Machines (SVM) untuk Klasifikasi Pengenalan Citra Wajah Silitonga, Parasian DP; Damanik, Romanus
Jurnal ICT : Information Communication & Technology Vol 20, No 1 (2021): JICT-IKMI, Juli 2021
Publisher : STMIK IKMI Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36054/jict-ikmi.v20i1.354

Abstract

Abstract- The study of face recognition is one of the areas of computer vision that requires significant research at the moment. Numerous researchers have conducted studies on facial image recognition using a variety of techniques or methods to achieve the highest level of accuracy possible when recognizing a person's face from existing images. However, recognizing the image of a human face is not easy for a computer. As a result, several approaches were taken to resolve this issue. This study compares two (two) machine learning algorithms for facial image recognition to determine which algorithm has the highest level of accuracy, precision, recall, and AUC. The comparison is carried out in the following steps: image acquisition, preprocessing, feature extraction, face classification, training, and testing. Based on the stages and experiments conducted on public image datasets, it is concluded that the SVM algorithm, on average, has a higher level of accuracy, precision, and recall than the k-NN algorithm when the dataset proportion is 90:10. While the k-NN algorithm has the highest similarity in terms of accuracy, precision, and recall at 80%: 20% and 70%: 30% of 99.20. However, for the highest AUC percentage level, the k-NN algorithm outperforms SVM at a dataset proportion of 80%: 20% at 100%.
Penerapan Algoritma Decision Tree Untuk Memprediksi Penerima Bantuan Keluarga Harapan Agus Bahtiar; Parasian DP Silitonga
Jurnal ICT : Information Communication & Technology Vol 19, No 1 (2020): JICT-IKMI, Juli 2020
Publisher : STMIK IKMI Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36054/jict-ikmi.v19i1.93

Abstract

The Family of Hope Program (PKH) is a poverty reduction program in the education and public health aspects provided by the government either directly or indirectly. The government continues to make efforts in order to educate the community through social assistance programs to tackle the poor. In order to create a smart society, the government should make programs that are empowering so that people can solve their own problems. There are many in Indonesia who receive the family hope program (PKH), one of which is in the Cirebon district. Problems often occur with the empowerment assistance program from the government, one of which is the PKH assistance, which still does not target the residents who receive the assistance. The emergence of this problem, due to the ineffective data verification in determining which citizens are entitled to receive PKH assistance, this has resulted in many very poor people who do not receive PKH assistance and those classified as capable are still given PKH assistance. Therefore, it is necessary to conduct a study of PKH beneficiary data, so that the results of the analysis can be used as a reference for whether or not residents are eligible to receive PKH assistance. The research that was conducted to predict the data of recipients of the expected family assistance using the data mining classification method using the C4.5 algorithm. The results of the data mining process are used as evaluation material for the government. After testing with the C4.5 algorithm, the test results for the best parameter of the C4.5 algorithm are criterion = accuracy, confidence = 0.25 and a minimum gain = 0.1 to produce an accuracy value of 98.30%
Pengenalan Rumah Adat Sumatera Utara Menggunakan Augmented Rality Berbasis Android Parasian DP Silitonga; Destri Gultom; Irene Sri Morina
Jurnal ICT : Information Communication & Technology Vol 19, No 2 (2020): JICT-IKMI, Desember 2020
Publisher : STMIK IKMI Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36054/jict-ikmi.v20i2.276

Abstract

This study aims to increase knowledge through multimedia-based learning by utilizing information technology, namely Augmented Reality (AR) in knowing the types of traditional houses in North Sumatra province.Augmented reality (AR), is the appearance of real-world dimensions with virtual worlds in real time. In contrast to virtual reality which completely replaces what is in the real world, augmented reality is a process of adding or completing virtual reality. Augmented reality depicts a three-dimensional object on a marker as a unique pattern so that it can be recognized by the object processing application. Augmented reality can be used to create a more interactive recognition environment where users can interact directly with objects in cyberspace. The augmented reality application produced in this study was developed using the Vuforia Software Development Kit. In addition, the resulting application runs on the android mobile platform, which is expected to provide easy access for users
Analisis Sentimen Kampus Merdeka Menggunakan Machine Learning Parasian DP Silitonga; Irene Sri Morina; Mitra Hasibuan; Uning Lestari
Jurnal ICT : Information Communication & Technology Vol 21, No 1 (2022): JICT-IKMI, Juli 2022
Publisher : STMIK IKMI Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36054/jict-ikmi.v21i1.480

Abstract

Analisis sentimen merupakan interpretasi dan klasifikasi emosi (positif, negatif, netral) pengguna tentang suatu subjek dalam data teks dengan menggunakan analisis teks. Dengan bantuan analisis sentimen, informasi yang tidak terstruktur yang dapat diubah menjadi data yang lebih terstruktur yang kemudian dapat digunakan menjelaskan opini masyarakat mengenai produk, merek, layanan, politik, atau topik lainnya. Terdapat beberapa metode yang dapat digunakan untuk melakukan analisis sentimen, salah satunya adalah machine learning. Machine learning digunakan sebagai tools untuk menghasilkan robot yang mampu mengklasifikasikan jenis sentimen dalam data tekstual. Merdeka Belajar Kampus Merdeka (MBKM), merupakan kebijakan Menteri Pendidikan dan Kebudayaan, yang bertujuan mendorong mahasiswa untuk menguasai berbagai keilmuan yang berguna untuk memasuki dunia kerja. Kampus Merdeka memberikan kesempatan bagi mahasiswa untuk memilih mata kuliah yang akan mereka ambil. Penelitian ini dilakukan untuk menghasilkan model machine learning dengan menggunakan metode Support Vector Machine (SVM) yang dapat digunakan untuk mengukur tingkat popularitas program kampus merdeka yang telah diluncurkan oleh Kementrian Pendidikan, Kebudayaan, Riset dan Teknologi Informasi Republik Indonesia berdasarkan data komentar atau opini masyarakat di media sosial.Berdasarkan penelitan yang dilakukan, ditemukan bahwa jumlah true positive rate adalah 270 record dikategorikan sebagai label positif dan false positive rate adalah 0 record dikategorikan sebagai label negatif. Kemudian jumlah true negative rate adalah 11 record dikategorikan sebagai label negatif dan false negative rate adalah 67 record dikategorikan sebagai label positif. Hasil pengujian data ditemukan bahwa tingkat akurasi algoritma SVM adalah sebesar 80,75%.
Sistem Informasi Reservasi dan Pembayaran Tagihan Hotel Menggunakan Payment Gateway Parasian D.P Silitonga; Evinri Mentari Parhusip
Jurnal ICT: Information Communication & Technology Vol. 22 No. 2 (2022): JICT-IKMI, December 2022
Publisher : LPPM STMIK IKMI Cirebon

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

Abstract

Hotel Reservation and Bill Payment Information System using Payment Gateway, is an information system that focuses on reservation data and payment systems using payment gateways entered by receptionists in business transactions. In the previous system, data was entered manually by taking notes in the guest book or agenda book. The risk is less effective and efficient management. Using a web[1]based information system, the input data of each transaction can be recapitulated quickly. Therefore, a website-based recording system is needed to improve the efficiency of the work system in hotels. The development of this system aims to help manage reservation and payment data more effectively and efficiently, so that transactions are recorded and stored properly. The system is created using the SDLC Waterfall method, which includes analysis of system needs, system design, system implementation, system testing and system maintenance. Website-based system, using PHP programming language with Laravel framework, Sublime Text editor, and Xampp as localhost, MySql Database. The existence of a web-based information system will overcome errors in the recording process manually and make it easier to make reports related to reservations for the finalization of computerized transaction data automatically.
Co-Authors Arifin, Bagas Adam Destri Gultom Evinri Mentari Parhusip Irene Sri Morina Irene Sri Morina Irene Sri Morina Jaya, Berkat Iman Matondang, Zekson Arizona Mitra Hasibuan Pardede, Ferri Ojak Immanuel Prima, Matra Purba, Doni El Rezen Romanus Damanik Samosir, Alfredo Simbolon, Rohani Sufi Yanti Tondang, Artur Marcel Turnip, Syarif Mayron Uning Lestari