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All Journal Jurnal Eksplora Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING
Meiliana, Komang Gita
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Computer Science & IT

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Audit SIMDIKLAT Balai Diklat Keagamaan Denpasar Menggunakan Framework COBIT 5 Meiliana, Komang Gita; Divayana, Dewa Gede Hendra; Dewi, Luh Joni Erawati
Jurnal Eksplora Informatika Vol 12 No 2 (2023): Jurnal Eksplora Informatika
Publisher : Institut Teknologi dan Bisnis STIKOM Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30864/eksplora.v12i2.1092

Abstract

BalaiIDiklatLKeagamaanLDenpasar merupakan salah satu instansi pemerintah berada di bawah Kementerian Agama di daerah. BDK Denpasar merupakan salah satu dari 14 BDK di seluruh Indonesia. BDK mempunyai fungsi melakukan pelatihan kediklatan bagi Pegawai Negeri Sipil (PNS) ataupun Non-PNS di lingkungan KementerianLAgama. SIMDIKLAT berfungsi untuk mempermudah proses pelatihan atau kediklatan yang dilakukan oleh panitia Aplikasi SIMDIKLAT terus dilakukan perkembangan, karena ditemukan kendala selama penggunanan salah satunya alat unduh data, dimana belum ada opsi kustomisasi yang sesuai dengan kebutuhan pengguna, seperti nama, NIP serta jabatan. Hal tersebut menyebabkan kesulitan bagi panitia untuk mengakses informasi yang spesifik saat melakukan unduhan data peserta. Sehingga panitia harus meminta pembaruan data ke instansi terkait, yang memerlukan waktu lama. Dari kendala yang ditemukan perlu dilakukanmAudit Sistem Informasi pada aplikasi SIMDIKLAT, untuk mengetahui penerapan penggunan SIMDIKLAT telah sesuai dengan visi misi yang diinginkan. Audit dilakukan menggunakan framework COBIT 5, dengan melakukan pemetaan pada 5 domain dan 37 proses pada COBIT 5. Langkah awal dilakukan penelitian ini dilakukan dengan observasi, studi literatur, wawancara, dan penyebaran kuisioner. Kuisioner yang disebarkan kemudian dipetakan sehingga memperoleh hasil audit EDM04, APO01, APO07, BAI02, DSS05,dan MEA01. Kesenjangan yang diperoleh pada aplikasi SIMDIKLAT EDM04 bernilai 2 gap, APO01, APO7,BAI02, DSS05 dan MEA01 kesenjangan bernilai 1 gap.
Effectiveness of AdaBoost and XGBoost Algorithms in Sentiment Analysis of Movie Reviews Lestari, I Gusti Ayu Nandia; Dewi, Ni Made Rai Masita; Meiliana, Komang Gita; Aryanto, I Komang Agus Ady
Journal of Applied Informatics and Computing Vol. 9 No. 2 (2025): April 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i2.9077

Abstract

Currently there are many entertainment platforms that provide various movies, TV shows, games, and other content. These platforms usually offer a variety of features, one of which is reviews. Review data written by viewers plays an important role in influencing public interest in the film. However, the increasing number of reviews makes it difficult to assess the sentiment of the film quickly and accurately. This highlights the need for a system that can analyze reviews based on sentiment, making it easier for viewers to evaluate the film and supporting the entertainment industry in understanding the needs of the audience. Therefore, this study develops a sentiment analysis model to identify whether a review contains positive or negative sentiment using machine learning algorithms. The data used to build the model is obtained from user reviews of a film on the IMDb platform. This dataset is available on Kaggle with 50,000 movie reviews in text format. The characteristics of the data include two columns: review_text and sentiment. The methods used to create the classification model are AdaBoost and XGBoost. The data preprocessing process includes several stages such as text cleaning, tokenization, stopword removal, lemmatization, and vectorization using TF-IDF to convert the review text into numeric form, as well as converting the positive and negative labels into 1 and 0. Based on the results of model training with cross-validation, the accuracy of the XGBoost model is 85% and AdaBoost is 77%. Feature selection showed an improvement in the XGBoost model's accuracy from 85% to 86%, while the AdaBoost model's performance remained stable at 77%. Thus, it can be concluded that the XGBoost model demonstrates better performance than the AdaBoost model in sentiment classification.
Co-Authors Aryanto, I Komang Agus Ady Dewa Gede Hendra Divayana, Dewa Gede Hendra Dewi, Ni Made Rai Masita Lestari, I Gusti Ayu Nandia Luh Joni Erawati Dewi