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All Journal Jurnal Ilmiah Matrik Jurnal Eksplora Informatika J-SAKTI (Jurnal Sains Komputer dan Informatika) KOMPUTEK MULTINETICS Infotech: Journal of Technology Information Bulletin of Computer Science Research J-SAKTI (Jurnal Sains Komputer dan Informatika) International Journal Software Engineering and Computer Science (IJSECS) Acta Pedagogia Asiana JURNAL TEKNIK INFORMATIKA DAN KOMPUTER The Indonesian Journal of Computer Science
Kamayani, Mia
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Mechanical Engineering Other

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Evaluation of Computer Lab at XYZ Institution using BAI & DSS Domains of COBIT 2019 Febriawan, Dimas; Kamayani, Mia; Imanda, Rahmi
Jurnal Ilmiah Matrik Vol. 27 No. 2 (2025): Jurnal Ilmiah Matrik
Publisher : Direktorat Riset dan Pengabdian Pada Masyarakat (DRPM) Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33557/wsbeqv24

Abstract

This research aims to measure the IT governance implementation in the computer laboratory at XYZ Institution using COBIT 2019 framework. Based on the scope and the problems that were identified, BAI and DSS aspects are the domains chosen to measure the implementation of the IT governance. The methods for this research are focus group discussion and field assessment. The BAI and DSS domains consist of 16 objectives, which are then divided into 104 practices and then divided further into 535 activities. These 535 activities are the processes that we have to determine for each capability level. After determining the capability levels for each process, we summarized the values and then evaluated the average values for each objective. These average values are the values that we used to determine the capability levels for each objective. We presented the result of our self assessment using a radar diagram. XYZ Institution is still in the starting phase of having good IT governance. This condition is reflected by the achievement of each objective’s capability levels ranging from 1 to 2. In addition to this condition, there is only one objective that meets the Institution’s capability level target of 3.
Analis Sentimen Aplikasi Maskapai Penerbangan Lion Air Menggunakan Metode SVM dan Naïve Bayes Sulistiawati, Risa; Kamayani, Mia
The Indonesian Journal of Computer Science Vol. 13 No. 3 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i3.3836

Abstract

Lion Air App is a flight ticket purchase application launched on October 21, 2014. It can be downloaded and used anywhere, anytime. Lion Air App application is available on the Google Play Store and also the Appstore, which aims to facilitate users in the process of purchasing airplane tickets online. online. In several news articles reporting that Lion Air is the world's worst airline. in the world. However, it needs to be realized that the Lion Air application also has many users who give positive, negative and neutral reviews due to several factors. neutral due to the existence of several reviews presented in the Play Store application. This problem was researched for sentiment analysis to get a customer satisfaction rating for the Lion Air application. Lion Air application with the acquisition of 2000 data. In this research, Support Vector Machine (SVM) calculation and Naive Bayes calculation were compared using 80% training ratio and 20% test ratio. In this consideration, 795 positive opinions and 805 negative opinions were used. used, where Support Vector Machine (SVM) with Bigram features became the most superior method with 99.23% precision. method with 99.23% precision, 83.03% recall, 91.75% accuracy, F-1 score of 90.51%.         
Deteksi hate speech pada kolom komentar TikTok dengan menggunakan SVM Ariska, Amelia; Kamayani, Mia
The Indonesian Journal of Computer Science Vol. 13 No. 3 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i3.3982

Abstract

The TikTok application provides numerous features, including the comment section for users to interact with each other. Users can exchange their opinions openly through the comment section. However, as the interaction or exchange of opinions among users increases, the use of hate speech, consciously or unconsciously, remains prevalent. Hate speech refers to actions by an individual or group that can incite criminal acts, thereby harming others. This study aims to identify the use of hate speech in TikTok comment sections using the SVM algorithm and to compare two libraries used in the labeling process to observe the performance of the SVM algorithm model. The labeling process employs a lexicon-based approach. The dictionaries used in this study are the Inset lexicon and VaderSentiment. The SVM algorithm is used as the model to test the evaluation results. The results obtained using the Inset lexicon labeling show an accuracy of 82%, while the second labeling method using VaderSentiment yields an accuracy of 96.21%.
Implementasi CMS Pada Media Pembelajaran Mengenal Alat Musik Tradisional Indonesia Sinduningrum, Estu; Suhendra, Renadi Fadino; Kamayani, Mia
MULTINETICS Vol. 4 No. 2 (2018): MULTINETICS Nopember (2018)
Publisher : POLITEKNIK NEGERI JAKARTA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32722/multinetics.v4i2.1345

Abstract

Traditional musical instruments were born and developed throughout the archipelago in Indonesia, which is still a hereditary habit in the community to date. This musical instrument is spread in almost all corners of the country and each region has different characteristics. Children in learning and knowing traditional musical instruments are less desirable because traditional musical instruments are considered outdated and old-fashioned, so they are reluctant to learn them. children are made media to introduce traditional CMS-based musical instruments and musical instruments made in 3D, so children want to know and learn traditional musical instruments. System modeling on the construction of this CMS use UML (Unified Modelling Language) with tool activity diagram, use case, class diagram and developed using the method waterfall which is run on system. System testing is permormed using alpha testing and beta testing, for beta testing is done by distributing the system testing questionnaire to the 15 listed respondent. For the result of the percentage is 86,67%, which means the result of this CMS assessment can be categorized very well.
Perbandingan Pelabelan Data dalam Analisis Sentimen Kurikulum Proyek di platform TikTok: Pendekatan Naïve Bayes Pratiwi, Anissya Agsani; Kamayani, Mia
Eksplora Informatika Vol 14 No 1 (2024): Jurnal Eksplora Informatika
Publisher : Institut Teknologi dan Bisnis STIKOM Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30864/eksplora.v14i1.1093

Abstract

Penelitian ini fokus pada analisis sentimen mahasiswa terhadap perubahan kurikulum berbasis proyek di tingkat pendidikan tinggi yang menghilangkan kewajiban skripsi, Data sentimen diekspresikan melalui platform media sosial TikTok, dan algoritma Naïve Bayes digunakan untuk mengklasifikasikan sentimen menjadi positif atau negatif. Proses penelitian mencakup pengambilan data, pembersihan data, preprocessing data, pelabelan data, hingga klasifikasi menggunakan algoritma Naive Bayes. Penelitian ini melibatkan dua tahap pelabelan dalam 913 data: pelabelan pertama manual menghasilkan 510 sentimen positif dan 403 negatif, sementara pelabelan kedua otomatis dengan RapidMiner menghasilkan 415 sentimen positif dan 498 negatif. Beberapa mahasiswa memberikan ulasan positif menganggap hal ini sebagai langkah inovatif untuk persiapan di dunia kerja. Meskipun beberapa merasa khawatir dengan tingkat kesulitan yang lebih tinggi. Hasil penelitian menunjukkan mayoritas tanggapan positif terhadap kurikulum berbasis proyek, dengan nilai pelabelan manual mencapai accuracy 93.98%, precision 100%, recall 87.99%. Sedangkan pelabelan otomatis dengan Rapidminer memperoleh nilai accuracy 70.41%, precision 80.15%, recall 69.96%.
ANALISIS SENTIMEN TERHADAP ULASAN PENGGUNAAN SHOPEE MELALUI TWEET PADA TWITTER MENGGUNAKAN ALGORITMA NAÏVE BAYES Muflih, Hilmy Zhafran; Al Assyam, Hafizh Dhery; Pangestu, Faisal Akbar; Kamayani, Mia
Jurnal Teknik Informatika dan Komputer Vol. 2 No. 2 (2023): Jurnal Teknik Informatika dan Komputer
Publisher : Universitas Muhammadiyah Prof. DR. HAMKA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22236/jutikom.v2i2.12199

Abstract

The increasing use of the internet among the public is because it is a means to carry out various activities, one of which is buying and selling online or known as e-commerce. One of the largest e-commerce in Indonesia is Shopee. Shopee offers various features for its users. The large number of shopee users results in the large number of responses given to shopee, so the researcher wants to carry out a sentiment analysis process regarding user responses to shopee, whether the response of shopee users is negative or positive. The responses or opinions of Shopee users are taken from tweets in the Twitter application. Tweets typed and written and published by Twitter users about shopee. In this study, researchers used the RapidMiner application to collect tweets data from Twitter users and to apply the Naïve Bayes algorithm. The researcher collected 200 data regarding shopee from Twitter. The results obtained from sentiment analysis using the Naïve Bayes algorithm get 78% negative sentiment and 22% positive sentiment from 200 datasets. The process of testing the Naïve Bayes algorithm using the confusion matrix obtains an accuracy value of 77.50%.
Co-Authors Al Assyam, Hafizh Dhery Arif, Zainul Ariska, Amelia Attyyatullatifah, Iqlimah Ayyubi, Reza Al Azhar, Nur Chalik Azzahra, Nabila Dewy, Avry Liyanah Eka Novianti, Eka Febriawan, Dimas Firman Noor Hasan Komarudin, Ahmad Mahendra, Ristian Muflih, Hilmy Zhafran Pangestu, Faisal Akbar Pratiwi, Anissya Agsani Rahman Malik, Luqman Abdur Rahmi Imanda Siduningrum, Estu Sinduningrum, Estu Suhendra, Renadi Fadino Sulistiawati, Risa Utami, Arneitta Dwicahya Yunita, Rani