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All Journal Voteteknika (Vocational Teknik Elektronika dan Informatika) Jurnal Edukasi dan Penelitian Informatika (JEPIN) JOIV : International Journal on Informatics Visualization JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) JSAI (Journal Scientific and Applied Informatics) Building of Informatics, Technology and Science Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Progresif: Jurnal Ilmiah Komputer Infotekmesin JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Teknik Informatika (JUTIF) Bulletin of Computer Science Research Jurnal Abdimas Bina Bangsa Algoritme Jurnal Mahasiswa Teknik Informatika Jurnal Atma Inovasia Jurnal INFOTEL Infact: Jurnal Sains dan Komputer
Sunneng Sandino Berutu
Universitas Kristen Immanuel
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Environmental Science Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Social Sciences Other

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Uji Kinerja Metode Deep Convolutional Neural Networks Untuk Identifikasi Gangguan Daya Listrik Sunneng Sandino Berutu
Infotekmesin Vol 13 No 2 (2022): Infotekmesin: Juli, 2022
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v13i2.1541

Abstract

The identification model development of the power disturbance signals with the deep convolutional neural networks (CNNs) method involves a large amount of data. However, the real signal data is limited. Therefore, researchers employ synthetic signal data. These signals can be generated by the formula IEEE standardized. In these formulas, two categories have a similar formula i.e interruption and sag. The difference is only in the intensity parameter (α). This paper analyzed the model performance of identifying those disturbances where the intensity values are set differently for training and testing datasets based on the upper bound value α of sag and the lower bound value α of interruption. Several noise levels are included in the signals. So, there are several datasets with noises in this simulation. Furthermore, those datasets are trained using the model based on deep CNN. The test results show that the true positive (TP) of the model's performance in identifying the interruption signal is 93.54% and the sag signal is 78.78%. In addition, the performance of the model using a dataset without noise obtained a high percentage in accuracy, precision, and f1-score parameters with 92.4%, 97.4%, and 92,76%, respectively.
Pengembangan Model Klasifikasi Sentimen Dengan Pendekatan Vader dan Algoritma Naive Bayes Terhadap Ulasan Aplikasi Indodax Zendrato, Agus Dirgahayu; Berutu, Sunneng Sandino; Sumihar, Yo’el Pieter; Budiati, Haeni
Journal of Information System Research (JOSH) Vol 5 No 3 (2024): April 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v5i3.5050

Abstract

Cryptocurrency trading applications such as Indodax have grown rapidly, the understanding of user sentiment towards the platform is still lacking, so it is interesting to analyze user sentiment towards the platform. To measure sentiment, this research proposes a combined approach of Vader and Naïve Bayes methods. The data used is a collection of user comments on the google play store platform related to user experience using Indodax. The Vader method is used to analyze sentiment directly from the comment text, while Naïve Bayes is adopted to improve accuracy in sentiment classification. The sentiment analysis process involves various steps, starting from data preparation, data pre-processing, labeling of training and testing data and performance evaluation of the Naive Bayes model. At the sentiment analysis stage with the Vader Sentiment method, the positive category obtained the highest percentage of 63.5%, followed by the neutral category at 18.9% and negative at 17.6%. Meanwhile, based on the performance evaluation of the Naïve Bayes model, the accuracy value is 78% while the highest precision value is achieved by the negative sentiment category at 80% and recall in the positive sentiment category at 44%.
A Conversion of Signal to Image Method for Two-Dimension Convolutional Neural Networks Implementation in Power Quality Disturbances Identification Berutu, Sunneng Sandino; Chen, Yeong-Chin; Wijayanto, Heri; Budiati, Haeni
JOIV : International Journal on Informatics Visualization Vol 6, No 4 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.4.1529

Abstract

The power quality is identified and monitored to prevent the worst effects arise on the electrical devices. These effects can be device failure, performance degradation, and replacement of some device parts. The deep convolutional neural networks (DCNNs) method can extract the complexity of image features. This method is adopted for the power quality disruption identification of the model. However, the power quality signal data is a time series. Therefore, this paper proposes an approach for the conversion of a power quality disturbance signal to an image. This research is conducted in several stages for constructing the approach proposed. Firstly, the size of a matrix is determined based on the sampling frequency values and cycle number of the signal. Secondly, a zero-cross algorithm is adopted to specify the number of signal sample points inserted into rows of the matrix. The matrix is then converted into a grayscale image. Furthermore, the resulting images are fed to the two-dimension (2D) CNNs model for the PQDs feature learning process. When the classification model is fit, then the model is tested for power quality data prediction. Finally, the model performance is evaluated by employing the confusion matrix method. The model testing result exhibits that the parameter values such as accuracy, recall, precision, and f1-score achieve at 99.81%, 98.95%, 98.84, and 98.87 %, respectively. In addition, the proposed method's performance is superior to the previous methods. 
Implementasi dan Pelatihan SIWAREK serta Solar Home System bagi Pokdarwis Opak Grembyangan Setyowati, Emerita; Sunneng Sandino Berutu; Susi Siswati
Jurnal Atma Inovasia Vol. 6 No. 1 (2026)
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/jai.v6i1.12870

Abstract

 The Tourism Awareness Group or Kelompok Sadar Wisata (Pokdarwis) of Mutihan, Madurejo Village, Prambanan, is pioneering the development of a tourism area along the Opak River. The main challenges faced include the lack of lighting facilities and difficulties in operating food stalls, although the buildings are already available. The community service team, in collaboration with local partners, initiated two main programs: the installation of a Solar Home System (SHS) to provide access to clean energy and reduce dependency on the state electricity grid (PLN), and training on financial management and digitalization for stall operations. The implementation methods included program socialization, financial management training, development of a stall information system, SHS installation, SHS operation training, as well as program monitoring and evaluation. As a result, a stall financial system named Sistem Informasi Keuangan Warung Opak (SIWAREK) was developed, and two SHS units were installed at the tourism site to supply renewable energy. The financial management and information system training provide a strong foundation for Pokdarwis to operate the food stalls. The SHS can be utilized for night lighting, charging visitors’ mobile phones and laptops, and also serve as a tourism icon showcasing the use of renewable energy
Text Mining dan Klasifikasi Sentimen Berbasis Naïve Bayes Pada Opini Masyarakat terhadap Makanan Tradisional Sunneng Sandino Berutu
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 4 No. 2 (2022): Desember 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i2.5138

Abstract

Indonesia has several famous traditional foods and is available in some cities. In addition, several international foods also are interesting to Indonesian. This article analyzes the netizen sentiment for these food categories where the data source is Twitter. The foods are rendang, sate, gudeg, pizza, hamburger, and spaghetti. The text mining approach is adopted to process data. The research steps are data crawling, cleaning, filtering, translating, and splitting. Furthermore, the classifier model based on the Naïve Bayes algorithm is developed. The analysis result shows that the gudeg food reaches a high percentage of positive sentiment with 57,9.  Then, the high rate of negative sentiment is achieved by the rendang food with 21,9 %. Moreover, hamburger food obtains a high percentage of neutral sentiment. Meanwhile, the evaluation of classifier model performance shows that the model with the hamburger dataset achieves a high score for accuracy, precision, and recall parameters with 0.72, 0.72, and 0.68 sequentially. 
Analisis Sentimen Berbasis ASOQE dan Taksonomi pada Program MBG di X mendrofa, victor crisman; Berutu, Sunneng Sandino; Budiati, Haeni
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 6 No 2 (2026): April 2026 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v6i2.15636

Abstract

The Free Nutritious Meal (MBG) program faces implementation challenges regarding distribution, menu quality, and budget sustainability, sparking diverse public discourse on social media. This study analyzes public sentiment toward the MBG program using an Aspect-Opinion-Qualifier Extraction (ASOQE) approach based on policy taxonomy. The dataset was obtained from X (formerly Twitter) via web scraping and processed through standardized text preprocessing. Automatic annotation used a lexicon-based BIO labeling approach to generate a silver-standard dataset. The classification model was trained using an IndoBERT-BiLSTM architecture to identify contextual aspects and opinions. Inference results were mapped into five sentiment classes and five policy dimensions: nutritional quality, implementation, social impact, policy, and effectiveness. Evaluation showed excellent performance, with F1-scores exceeding 0.98. Findings reveal that social impact and implementation dimensions dominate public discourse, showing significantly positive sentiment. This research demonstrates the potential of Aspect-Based Sentiment Analysis as a data-driven tool for comprehensive public policy evaluation.
Analisis Sentimen Berbasis Aspek Program Koperasi Desa Merah Putih Menggunakan IndoBERT gea, yuris mardayani; Berutu, Sunneng Sandino; Jatmika, Jatmika
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 6 No 2 (2026): April 2026 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v6i2.15711

Abstract

The Koperasi Desa Merah Putih program is a strategic initiative requiring evaluation through public perception monitoring. This study employs Aspect-Based Sentiment Analysis (ABSA) using the IndoBERT transformer model via a two-stage approach: aspect-opinion extraction using BIO labeling (Token Classification) and sentiment polarity determination (Sequence Classification). A dataset of 12,013 entries from Platform X underwent systematic preprocessing and was trained using an 80:20 stratified split to ensure label balance. Model performance, evaluated through accuracy, precision, recall, and F1-score, demonstrated high reliability with 79% accuracy. Collectively, the analysis identified 4,917 neutral, 3,961 negative, and 3,135 positive opinions. Specifically, the "Economy" aspect recorded 1,673 positive opinions, reflecting public optimism regarding the program's economic impact. These results confirm that Deep Learning-based approaches provide granular insights into policy effectiveness, serving as an accurate decision-support instrument for cooperative program managers at the village level to improve policy implementation based on data-driven evidence.
Implementasi Aspect-Based Sentiment Analysis Berbasis IndoBERT Pada Program Sekolah Rakyat Marunduri, Tik Tanika; Berutu, Sunneng Sandino; Jatmika, Jatmika
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 6 No 2 (2026): April 2026 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v6i2.15734

Abstract

The Sekolah Rakyat program is a strategic Ministry of Social Affairs initiative requiring continuous evaluation through public perception monitoring. This study employs Aspect-Based Sentiment Analysis (ABSA) using the IndoBERT transformer model via a two-stage approach: aspect-opinion extraction using BIO labeling (Token Classification) and sentiment polarity determination (Sequence Classification). A dataset of 14,787 entries from Platform X underwent systematic preprocessing and was trained using an 80:20 stratified split to ensure label balance. Model performance demonstrated high reliability, achieving 86% accuracy and stable F1-scores. Collectively, the analysis identified 8,454 neutral, 4,062 positive, and 2,271 negative sentiments. The results reveal that educational aspects, specifically regarding students, are the primary focus of public discourse, dominated by neutral sentiment. These findings confirm that Deep Learning-based approaches provide granular insights into policy effectiveness, serving as an accurate decision-support instrument for the government to evaluate educational policies comprehensively based on data-driven evidence.
Identifikasi Tingkat Intensitas Opini dalam Analisis Sentimen Berbasis Aspek Menggunakan Enhanced Triplet Extraction Jimmy Richardo Chastelo B, Gabriel; Berutu, Sunneng Sandino; Budiati, Heani
Bulletin of Computer Science Research Vol. 6 No. 3 (2026): April 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i3.1074

Abstract

Conventional sentiment analysis often overlooks variations in the intensity of opinions within text reviews. This is due to the limitations of the Aspect-Based Sentiment Analysis (ABSA) approach, which is restricted to three main triplet components. This study aims to develop and expand the Aspect-Sentiment-Opinion Triplet Extraction (ASOTE) framework to extract entity relationships and sentiment polarity by integrating opinion intensity detection. This study implements the ABSA approach by expanding the triplet structure into four components: aspect, opinion, intensifier, and sentiment (Enhanced Triplet). Data was collected via web scraping of Twitter (X) comments related to the Free Nutritious Meals program, which served as a case study to test the model’s ability to analyze public sentiment. The data then undergoes pre-processing and BIO Tagging, and is classified using a fine-grained sentiment approach to capture the nuances of emotional intensity in greater detail. A Transformer-based model, namely IndoBERT, was used to understand the context and intensity of meaning in the Indonesian language. Evaluation results on the test data show that the model achieved an accuracy of 88% and an average F1-score of 0.88 in sentiment polarity classification between entities, indicating strong model performance. These results demonstrate that providing a framework that is more sensitive to the intensity of opinions when classifying the nuances of public sentiment is a highly effective solution. 
Contrastive Learning pada IndoBERT untuk Analisis Sentimen Kebijakan Makan Bergizi Gratis Dwi Dian Sari Nonibenia Hia; Sunneng Sandino Berutu; Jatmika
JSAI (Journal Scientific and Applied Informatics) Vol 9 No 1 (2026): Januari
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v9i1.9963

Abstract

Transformer-based language models such as IndoBERT still face limitations in topic and sentiment analysis of short social media texts, particularly due to embedding anisotropy, semantic overlap between topics, and limited sensitivity to implicit sentiment intensity. This study aims to evaluate the effectiveness of integrating SimCSE-based contrastive learning to optimize IndoBERT vector representations for sentiment analysis of the “Free Nutritious Meals” public policy. A comparative experimental approach was employed using an equal number of topics (three topics) and evaluated through BERTopic and Aspect-Based Sentiment Analysis (ABSA). The results demonstrate that the contrastive learning–based model substantially improves cluster separability, indicated by an increase of more than 1000% in the Silhouette Score compared to the baseline model, along with a reduction in topic overlap of approximately 40–50%. In addition, topic keyword diversity increased by more than 75%, yielding more informative and interpretable topic representations. In aspect-based sentiment analysis, the contrastive model exhibited approximately a 50% improvement in sensitivity to sentiment intensity and achieved perfect classification of implicit high-confidence sentiments that were previously misclassified as neutral by the baseline model. These findings confirm that contrastive learning–based embedding optimization effectively addresses the limitations of conventional embeddings and enhances the quality of topic modeling and aspect-based sentiment analysis for Indonesian social media texts.
Co-Authors Aninda Astuti Aninda Astuti Bobby Girsang Budiati, Heani Chen, Yeong-Chin Dachi, Christian Moris Dwi Dian Sari Nonibenia Hia Emerita Setyowati Febe Maedjaja Febe Maedjaja Fornieli Gulo gea, yuris mardayani Haeni Budiati Heri Wijayanto Indriyani Hulu Jatmika Jatmika Jatmika Jatmika Jatmika Jatmika Jatmika, Jatmika Jatmitka, Jatmitka Jimmy Richardo Chastelo B, Gabriel Juli Supriyanto Gea Kambayo, Andreas Noprianto Kambayo Kevin, Kevin Miracle Satoko Kharisteas Josan Sedi Krisna Putri Telaumbanua Kristian Juri Damai Lase Laia, Yardiana Lase, Anan Sosmita Linda Kristiani Zebua Marunduri, Tik Tanika mendrofa, victor crisman Mika, Jatmika Nauli, Sumitta Nehe, Pius Hermanto Nshimiyimana, Aristophane Rahman, Mosiur Reonardh Sibarani Retno Palupi Sispianygala, Aprilia Siswati, Susi Sumihar, Yo’el Pieter Tri Elsa, Maria Tri Yuli Yanto Veren, Yolanda Yuan, Anita Zakaria, Immanuel Richie De Harjo Zebua, Yuwinda Hartati Zendrato, Agus Dirgahayu