Article Per Year (5 Year)
p-Index From 2020 - 2025
0.61
P-Index
This Author published in this journals
All Journal Proceedings Konferensi Nasional Sistem dan Informatika (KNS&I) Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Informatics Nivedita
Putu Kussa Laksana Utama, Putu Kussa Laksana
STMIK STIKOM Bali
Computer Science & IT Control & Systems Engineering

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
Implementasi Metode Neural Network Pada Perancangan Pengenalan Pola Plat Nomor Kendaraan Utama, Putu Kussa Laksana
Proceedings Konferensi Nasional Sistem dan Informatika (KNS&I) 2015
Publisher : Proceedings Konferensi Nasional Sistem dan Informatika (KNS&I)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (373.205 KB)

Abstract

Semakin majunya teknologi informasi dan komputer sekarang ini diharapkan dapat membantu masyarakat untuk lebih nyaman saat meninggalkan kendaraannya serta membantu pihak kepolisisan untuk dapat menyingkapi atau minimal mencegah kejahatan atau tidak kriminalitas yang terjadi dewasa ini. Untuk meminimalisir tindak kriminal yang terjadi maka banyak metode yang dilakukan untuk pengamanan parkir di berbagai tempat. Salah satunya adalah menggunakan metode neural network. Dimana metode ini bisa dipergunakan untuk mengenal plat nomor kandaraan yang dipakai oleh pengendara itu sendiri. Dan tingkat kesalahannya pun sangat rendah karena memilikin kemampuan mengolah gambar menjadi data sangat tinggi. Neural Network memiliki sejumlah besar kelebihan dibandingkan dengan metode perhitungan lainnya yaitu kemampuan mengakusisi pengetahuan walaupun dalam kondisi ada gangguan dan ketidakpastian.hal ini karena neural network mampu melakukan generalisasi, abstraksi, dan ekstraksi terhadap properti statistic dari data.
Comparison of the Sentiment Analysis Model's Code Complexity and Processing Time: Sentiment analysis for tolerance and religious moderation in Indonesia: A Case Study Kartika, Luh Gede Surya; Utama, Putu Kussa Laksana; Budiastawa, I Dewa Gede; Rinartha, Komang
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 1 (2023): Articles Research Volume 7 Issue 1, 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i1.11894

Abstract

Text processing, which includes sentiment analysis, obviously demands a lot of resources. The extent to which resources are used to promote environmental conservation is directly impacted by code complexity or computational time. Twitter users' responses on the Indonesian Language of religious moderation and tolerance are used to test the model. The phases of doing the research include determining the research's needs, gathering data, text preprocessing (case folding, tokenizing, stopword removal, and stemming), word weighting with TF/IDF, and classification with Multinomial Naive Bayes (MNB) and Support Vector Machine (SVM), validation, and the last step was calculation of the code complexity and computation time. The validation results showed that the performance of the two models was still low, with an average accuracy of 75.5%. Based on computation time, SVM has a faster computational time than MNB. However, when compared in terms of the code complexity, the Cyclomatic Complexity in both models was the same because both models used existing libraries in Python Interpreter, and the complexity of the libraries cannot be calculated directly. Based on the Raw Metrics, it can be seen that MNB and SVM not significantly different in LOC, LLOC, and SLOC. It was evident that SVM has a greater Halstead Complexity than SVM in all measures when comparing the program code of MNB and SVM. Particularly on programming effort and volume metrics, SVM required more bits to run than an MNB program, and it also required more mental effort to convert a prepared SVM algorithm into a program than an MNB.
Analisis Kepuasan Mahasiswa Program Studi Informatika Terhadap Proses Pendidikan Kartika, Luh Gede Surya; Saskara, I Putu Adi; Pratama, Putu Adi; Utama, Putu Kussa Laksana; Sanjaya, I Gede Wahyu
Journal Informatics Nivedita Vol 1 No 1 (2024): Journal Informatics Nivedita
Publisher : Universitas Hindu Negeri I Gusti Bagus Sugriwa Denpasar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25078/nivedita.v1i1.4482

Abstract

This study aims to analyze the level of student satisfaction with learning services at the Informatics Study Program of the State Hindu University (UHN) I Gusti Bagus Sugriwa Denpasar in the Odd Semester 2023/2024. The research method used was a survey with 42 respondents from the class of 2023. Data was collected through questionnaires that included aspects of the use of learning technology, the suitability of evaluation materials with course objectives, lecturers' interpersonal relationships, student involvement in research, and lecturers' mastery of cutting-edge issues. The results of the analysis showed that the aspects of the use of media and learning technology, the suitability of exam materials/assignments with course objectives, and the ability of lecturers to interact with students obtained the highest satisfaction score (4.43) and belonged to the category of "Very Satisfied." However, the aspects of student involvement in lecturer research (4.17) and lecturer mastery of cutting-edge issues (4.18) obtained the lowest scores, even though the number means that belonged to the category “Satisfied/Good”. This study recommends increasing student involvement in research and strengthening lecturer competencies related to current issues.
Irrelevancy Detection in Multilingual Tourism Review Utama, Putu Kussa Laksana; Kartika, Luh Gede Surya; Pratama, I Putu Adi; Sanjaya, I Gede Wahyu; Saskara, I Putu Adi
Journal Informatics Nivedita Vol 1 No 1 (2024): Journal Informatics Nivedita
Publisher : Universitas Hindu Negeri I Gusti Bagus Sugriwa Denpasar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25078/nivedita.v1i1.4730

Abstract

This study investigates irrelevancy within multilingual tourism reviews, focusing on how off-topic or ambiguous user-generated content can undermine reliable insight for travelers. A consolidated dataset is constructed by combining a publicly available resource from Kaggle with additional posts acquired from X (formerly Twitter). Each review is manually labeled as relevant or ambiguous to capture instances where the content fails to clearly address travel or hotel-related topics. We employ a multilingual BERT embedding model to encode the diverse language inputs, enriched with a sentiment vector derived via knowledge distillation from twitter-xlm-roberta-base to DistilBERT. A gating mechanism then fuses the semantic and emotional signals, highlighting parts of each review most influenced by user attitudes. The final classification stage involves fine-tuning a BERT-based network to distinguish between unambiguous and ambiguous content. Experimental comparisons with a Monolingual BERT approach and a baseline (multilingual embedding without sentiment) reveal that incorporating sentiment features yields consistent improvements in accuracy, precision, recall, and F1-score. This outcome underscores the importance of capturing emotional cues to mitigate errors arising from partial dissatisfaction, unclear references, or cultural nuances. From a practical standpoint, the results point to potential applications in automated moderation, improved recommendation systems, and policy guidelines for tourism platforms. Overall, this work demonstrates that sentiment-aware, multilingual models can enhance detection of irrelevancy and ambiguity, fostering more trustworthy and context-rich online review ecosystems in the travel domain.
Co-Authors Budiastawa, I Dewa Gede I Komang Rinartha Yasa Negara I Putu Adi Pratama, I Putu Adi Luh Gede Surya Kartika, Luh Gede Surya Pratama, Putu Adi Sanjaya, I Gede Wahyu Saskara, I Putu Adi