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All Journal JATI (Jurnal Mahasiswa Teknik Informatika) Journal of Big Data Analytic and Artificial Intelligence
Praseptian M, Dikky
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Computer Science & IT

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Analisis Sentimen Kritik dan Saran Layanan RSUD Akhmad Berahim Tana Tidung menggunakan Metode Lexicon-Based Maulidia, Nurmala; Praseptian M, Dikky
Journal of Big Data Analytic and Artificial Intelligence Vol 8 No 1 (2025): JBIDAI Juni 2025
Publisher : STMIK PPKIA Tarakanita Rahmawati

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71302/jbidai.v7i2.65

Abstract

Rapidly developing information technology must become a significant component in its use in all human life to simplify work. This matter underlined the research with that title, which the author believes will be an element of service assessment, whether positive, negative, or neutral. Sentiment analysis is required while evaluating a service, particularly in hospitals. The Lexicon-Based method uses a dictionary or lexicon as a language basis. This method classifies a sentiment from each opinion so that a sentiment sentence can be classified as positive, neutral, or negative. The text data will then be calculated using a Lexicon-Based to produce service quality sentiment analysis. The research used 100 data, with a questionnaire distributed of as many as 90 data and a suggestion box of as many as 10 data for sentiment analysis. The research received data of 33 criticisms and 67 suggestions. The Lexicon-Based method also classifies data into positive, negative, and neutral. The designed system can assist hospitals in evaluating services.
PENERAPAN VIGENERE CIPHER DALAM MELINDUNGI DATA CITRA RONTGEN Arbain, Arbain; Fadlan, Muhammad; Praseptian M, Dikky
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 3 (2025): JATI Vol. 9 No. 3
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i3.13628

Abstract

Dalam dunia medis, citra rekam medis, seperti hasil pemeriksaan radiologi (rontgen), memegang peranan penting dan memerlukan perlindungan terhadap ancaman pemalsuan dan penyalahgunaan data. Seiring dengan perkembangan teknologi informasi, ancaman terhadap keamanan data citra semakin meningkat, tidak terkecuali citra rontgen. Vigenère cipher merupakan algoritma kriptografi klasik berbasis teknik substitusi, telah banyak digunakan untuk melindungi data, terutama data teks. Dalam penelitian ini, Vigenère cipher diterapkan untuk mengenkripsi dan mendekripsi citra, yakni citra rontgen. Hasil penelitian menunjukkan bahwa penerapan Vigenère cipher dalam kriptografi citra rontgen dapat dilakukan dengan baik. Proses enkripsi berhasil membuat citra rontgen menjadi tidak terlihat dengan jelas, meskipun citra tersebut tetap dapat ditampilkan, namun dengan perbedaan dari citra aslinya. Hal ini disebabkan oleh perubahan nilai RGB pada citra rontgen yang dienkripsi. Namun, eksperimen enkripsi dan dekripsi menunjukkan bahwa citra rontgen dapat kembali ke bentuk aslinya dengan sempurna, yang membuktikan bahwa proses enkripsi dan dekripsi berjalan dengan baik.
KLASIFIKASI KELAYAKAN MENERIMA BANTUAN SOSIAL MENGGUNAKAN METODE K-NEAREST NEIGHBOR Melpin, Melpin; Praseptian M, Dikky; Obert, Obert
Journal of Big Data Analytic and Artificial Intelligence Vol 7 No 1 (2024): JBIDAI Juni 2024
Publisher : STMIK PPKIA Tarakanita Rahmawati

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71302/jbidai.v7i1.54

Abstract

Data classification is the process of grouping data based on attributes (Congregation Employment, Congregation Dependents, Congregation Home Status, and Congregation Income). The problem currently occurring is that data collection on congregational social assistance recipients is often not on target, so that if social assistance enters the church, it is given to congregations who are actually less well off, but it is transferred to congregations who are well off, giving rise to confusion between one congregation and another. In this research the author used the K-Nearest Neighbor method or what is usually called KNN and measured algorithm performance using a confusion matrix to calculate accuracy, precision and recall. This researcher used 50 data that had been input via Google Form and then filled in the congregation from 50 data divided into 35 training data and 15 testing data. After the data has been input it will go through several stages, the first step is initialization where in the process of this initialization stage it changes the category value, the second stage is the process of dividing the value by the largest value in the attribute and the third stage is calculating the distance to then calculate the confusion matrix to determine accuracy, precision and recall. This research produces an application that can automatically determine which congregations are and are not worthy of receiving social assistance. From trials of 15 testing data, accuracy was 88.89%, precision 100% and recall 75%
Rekayasa Aplikasi Rekomendasi Pencarian Lokasi Dan Analisis Sentimen Menggunakan Penambangan Teks Febriyanti, Eka; Noviyantono, Endyk; Praseptian M, Dikky
Journal of Big Data Analytic and Artificial Intelligence Vol 7 No 2 (2024): JBIDAI Desember 2024
Publisher : STMIK PPKIA Tarakanita Rahmawati

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71302/jbidai.v7i2.66

Abstract

 This research aims to provide information and assessments about tourist attractions and culinary attractions that are popular on social media (Instagram and Twitter). The research process uses a text mining approach, starting with text processing (case folding, tokenizing, stopword removal, and stemming) to filter comments. Furthermore, weighting is carried out using the TF-IDF method to determine the relevance of words. The process of classifying comments by location name is carried out using the Naïve Bayes algorithm, followed by sentiment analysis to assess positive, negative, or neutral comments. The research application was built using PHP with a MySQL database and utilized a dataset of 73 comments (17 for tourism and 56 for culinary) collected from social media. The results of the study show that the system is able to produce recommendations for tourist and culinary attractions effectively based on data analysis
Co-Authors Arbain Arbain, Arbain Endyk Noviyantono Fadlan, Muhammad Maulidia, Nurmala Melpin, Melpin Nurwijayanti Obert, Obert