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All Journal Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi)
Alda Zevana Putri Widodo
Universitas Nusa Mandiri
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Implementasi Finite State Automata pada Mesin Otomatis Es Krim Alda Zevana Putri Widodo; Windu Gata; Sri Rahayu; Jordy Lasmana Putra; Laela Kurniawati
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 6 No 4 (2022): OCTOBER-DECEMBER 2022
Publisher : KITA Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v6i4.522

Abstract

Technology is increasingly developing to help various human activities. The application of technology that has a significant influence in the industrial sector is automatic machines. An automatic machine is a technology chain whose concept is to make manual activities automatic with the aim of carrying out production and making higher quality output. This study uses two methods, namely the analytical method by obtaining information from the relevant literature to analyze previously about the automatic ice cream machine and develop from the old version. The formal method is to analyze the operation of automatic machines starting from reading input to final completion to provide results from automatic machines and designing ice cream machine applications using visual studio applications. The results of this study have passed the JFLAP application testing and can be an option to analyze the automatic ice cream maker machine by recognizing the input patterns given by the user.
Pengkategorian Komentar Instagram Terhadap Layanan Akademik dan Non-Akademik Universitas Terbuka Rhini Fatmasari; Alda Zevana Putri Widodo; Valianda Farradillah Hakim; Windu Gata; Dedi Dwi Saputra
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 7 No 1 (2023): JANUARY-MARCH 2023
Publisher : KITA Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v7i1.669

Abstract

Instagram is one of the social media that has many users in Indonesia, where users are free to comment on whatever is going on, including being a form of online communication between campuses and their students. The number of topics and comments on an official Instagram account can be used as evaluation or learning material. The Open University is one of the campuses that has an official Instagram account with thousands of followers. In order to get an evaluation of academic and non-academic services, in this study a categorization analysis was carried out with 10,000 comment data taken from the official @univterbuka Instagram account. The data is categorized into 7 categories, then processed using 4 algorithms, namely SVM, Naïve Bayes, Random Forest and KNN. The highest accuracy in the category of teachers with the KNN method is 98.97% and the highest AUC is in the module category with the SVM method of 94.60%.
Analisis Perbandingan Algoritma Machine Learning Terhadap Sentimen Analis Pemindahan Ibu Kota Negara Arif Rahman Hakim; Windu Gata; Alda Zevana Putri Widodo; Oky Kurniawan; Arief Rama Syarif
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 7 No 2 (2023): APRIL-JUNE 2023
Publisher : KITA Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v7i2.701

Abstract

The Indonesian nation was enlivened with news about the relocation of the State Capital (IKN). The government's plan to move IKN is based on Indonesia's vision and mission in 2045, namely advanced Indonesia. Twitter is one of the microblogging communication tools used to express opinions. Various algorithms have been used to analyze sentiment towards an opinion such as Support Vector Machine, Naive Bayes and Random Forest. This study aims to compare the performance of three classification algorithms, namely Support Vector Machine, Naïve Bayes and Random Fores. The highest accuracy results are using the Support Vector Machine algorithm and added with the Synthetic Minority Oversampling Technique Method (SMOTE) feature of 82.82%, Precision 79.34%, Recall 88.75%, 87.78% and ROC AUC 82.82%. Naive Bayes accuracy is 81.18%, Precision 84.89%, Recall 75.86%, 80.13% and ROC AUC 81.18%. and Random Forest accuracy of 79.55, precision 84.48%, recall 72.39%, 77.97% and ROC AUC 77.55%.
Analisis Sentimen pada Komen Twitter Pawang Hujan Mandalika dengan Support Vector Machine (SVM) dan Naïve Bayes Rahmat Satria Buana; Windu Gata; Alda Zevana Putri Widodo; Hendra Setiawan; Khairunisa Hilyati
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 7 No 2 (2023): APRIL-JUNE 2023
Publisher : KITA Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v7i2.705

Abstract

Indonesia is a country with a majority Muslim population, but Indonesia is also rich in culture influenced by previous religions such as Hinduism and Buddhism. The rain handler by Muslims is considered shirk, but on the other hand also considers this ancestral culture that has always existed. With the advancement of human technology, it is easier to express opinions or opinions regarding a topic that is currently being discussed, for example, regarding the rain handler who acted at the Mandalika Circuit some time ago through social media. Twitter is one of the social media that is used as a forum to accommodate these opinions. In this study, the CRISP-DM (Cross Industry Standard Process for Data Mining) data mining methodology was used with Rapid Miner Version 9.10 with the Support Vector Machine (SVM) classification method and nave Bayes with SMOTE to improve accuracy. The application of the SVM method is 97.71 % with AUC 0.997 Positive Comments, and Using Naïve Bayes, the accuracy obtained is 93.41% accuracy with AUC 0.973 Positive Comments.
Co-Authors Arief Rama Syarif Arif Rahman Hakim Dedi Dwi Saputra Hendra Setiawan Jordy Lasmana Putra Khairunisa Hilyati Laela Kurniawati Oky Kurniawan Rahmat Satria Buana Rhini Fatmasari Sri Rahayu Valianda Farradillah Hakim Windu Gata