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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) 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) Jurnal Abdimas Bina Bangsa Jurnal INFOTEL Infact: Jurnal Sains dan Komputer
Sunneng Sandino Berutu
Universitas Kristen Immanuel
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering 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.
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 : STMIK 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. 
Data preprocessing approach for machine learning-based sentiment classification Sunneng Sandino Berutu; Haeni Budiati; Jatmika Jatmika; Fornieli Gulo
JURNAL INFOTEL Vol 15 No 4 (2023): November 2023
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v15i4.1030

Abstract

Public sentiment regarding a particular issue, product, activity, or organization can be measured and monitored with an application based on artificial intelligence. The data come from comments circulating on social media. However, the rules for writing comments on social media have yet to be standardized, so non-standard words often appear in these comments. Non-standard words affect the determination of sentiment into positive, negative, and neutral categories. Therefore, this study proposes a data preprocessing approach by inserting the Rabin-Karp algorithm to improve non-standard words. This research consists of several stages, namely crawling data, data preprocessing, feature extraction, model development (based on Naïve Bayes (NB), Support Vector Machine (SVM), and Decision Tree (DT) methods), and analysis of the results. The experimental results showed that the proposed approach influences the determination of the sentiment category composition. Then, model testing results showed that all models obtain the highest value in the Positive category for the precision parameter with a value 1. All models in the Neutral category obtain the highest value for the recall parameter, almost reaching 1. All models in the Neutral category achieve the highest value of the f1-score parameter, with an average value of 0.95. In general, the results of the performance analysis of the classification model showed that the NB and SVM-based models have better performance than the DT method.
Analisis Sentimen Masyarakat Terhadap Direktorat Jenderal Pajak Juli Supriyanto Gea; Haeni Budiati; Kristian Juri Damai Lase; Sunneng Sandino Berutu
Infact: International Journal of Computers Vol. 8 No. 01 (2024): Jurnal Sains dan Komputer
Publisher : Universitas Kristen Immanuel

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61179/jurnalinfact.v8i01.466

Abstract

One process of recognizing opinions in the form of text to determine emotions neutrally, positively, or negatively is called sentiment analysis. Sentiment analysis will explore people's emotions from texts conveyed through various social media. Directorate general of taxes as data taken from twitter for analysis was used by the author to support research. Data collection is done by searching and collecting data with tax keywords from twitter, so that it can be analyzed to determine public sentiment. From the data collected and analyzed, 40.5% were neutral, 39.4% positive, and 20.1% negative.
Implementasi Library Textblob dan Metode Support Vector Machine Pada Analisis Sentimen Pelanggan Terhadap Jasa Transportasi Online Laia, Yardiana; Berutu, Sunneng Sandino; Sumihar, Yo’el Pieter; Budiati, Haeni
Building of Informatics, Technology and Science (BITS) Vol 6 No 1 (2024): June 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i1.5090

Abstract

Online transportation services have become an inseparable part of human life today. This research aims to develop an effective sentiment analysis method to measure public opinion about the quality of online transportation services, which has a significant impact on company reputation and public acceptance of these services. In this research, we propose the use of TextBlob library to perform sentiment analysis of public opinion on online transportation services. This library allows to measure the positive, negative and neutral polarity and subjectivity of opinion text collected from Gojek, Maxim and Grab application reviews through Google Play Store. Sentiment analysis steps are carried out starting from data preparation, data pre-processing, data labeling using the Text Blob library. Furthermore, building a sentiment classification model based on the Support Vector Machine (SVM) algorithm through training and testing stages. Model testing results are evaluated with confusion matrix. The results of the analysis with textblob showed that online transportation received the highest positive sentiment of 40.1%, followed by neutral sentiment of 26.7% and negative sentiment of 25.2%. Meanwhile, the model performance measurement results show that the precision obtained the highest value in positive sentiment of 0.93. The recall parameter reaches the highest value in negative sentiment of 0.95 and f1-score in neutral and positive sentiment of 0.92. Thus, this research not only contributes to the development of sentiment analysis classification, but also has a significant practical impact in improving online transportation services and providing useful information to the public, thus encouraging innovation and continuous improvement in online transportation services.
Klasifikasi Sentimen Opini Pada Aplikasi Guitar Tuner Dachi, Christian Moris; Berutu, Sunneng Sandino; Jatmika, Jatmika
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 13, No 2: Agustus 2024
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v13i2.2041

Abstract

Current technological advances have an impact on various fields, including music applications. Guitar Tuna and other popular music applications can have an impact on public perception, customer decisions, and company reputation. This study analyzes user sentiment for Guitar Tuna using the Naive Bayes and Support Vector Machine (SVM) algorithms. Play Store is the place where data is obtained consisting of 626 neutral reviews, 234 positive reviews, and 2 negative reviews that have gone through several stages, namely preprocessing, classification, labeling, crawling, and evaluation models. The analysis resulted in a Naive Bayes accuracy of 0.95 and SVM of 0.97. The results of the Naive Bayes classification showed the highest value in the neutral sentiment precision parameter of 0.94. In the (SVM) method, positive sentiment gets the highest value at a precision of 1, while the highest recal and f1-score values are achieved by neutral sentiment with values 1 and 0.98 in the SVM method.Keywords: Naïve Bayes; Support Vector Machine; Sentiment Classification; Opinion Analysis; Guitar Tuna.                                                                      AbstrakKemajuan teknologi saat ini berdampak pada berbagai bidang, termasuk aplikasi musik. Guitar Tuna dan aplikasi musik populer lainnya dapat berdampak pada persepsi publik, keputusan pelanggan, dan reputasi perusahaan. Studi ini menganalisis sentimen pengguna untuk Guitar Tuna dengan menggunakan algoritma Naive Bayes dan Support Vector Machine (SVM). Play Store ialah tempat perolehan data terdiri dari 626 ulasan netral, 234 ulasan positif, dan 2 ulasan negatif yang sudah melakukan sejumlah tahapan yakni preprocessing, classification, labeling, crawling, serta evaluation model. Penganalisisan tersebut menghasilkan akurasi Naive Bayes sebesar 0,95 dan SVM sebesar 0,97. Hasil klasifikasi Naive Bayes menunjukan nilai tertinggi pada parameter precision sentimen netral sebesar 0,94. Pada metode (SVM), sentimen positif mendapatkan nilai tertinggi pada precision sebesar 1, sedangkan nilai tertinggi recal dan f1-score dicapai sentimen netral dengan nilai 1 dan 0.98 pada metode SVM. 
Pengembangan Aplikasi Sistem Rekomendasi Tempat Wisata Dengan Collaborative Filtering Sispianygala, Aprilia; Berutu, Sunneng Sandino; Jatmitka, Jatmitka
Progresif: Jurnal Ilmiah Komputer Vol 20, No 2: Agustus 2024
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v20i2.2044

Abstract

Tourists often face difficulties in choosing tourist destinations that match their preferences and interests among the many available options. The purpose of this research is to develop a tourism recommendation system application for Jakarta using the Collaborative Filtering method. This study will develop a tourist recommendation system for Jakarta using collaborative filtering with Python programming language and WxPython as the Graphical User Interface (GUI) framework using the Pycharm application. A dataset consisting of 985 entries has undergone pre-processing. The model evaluation results show that the Mean Squared Error (MAE) value is 0.7561 and the Root Mean Squared Error (RMSE) value is 1.0634. This indicates that the recommendation system's accuracy is approximately 91.60% based on the Mean Squared Error (MAE) and 88.18% based on the Root Mean Squared Error (RMSE).Key Word : Recommendation System; Collaborative Filtering; Jakarta; Tourist attraction; Pycharm AbstrakWisatawan seringkali menghadapi kesulitan dalam memilih tempat wisata yang sesuai dengan preferensi dan minat mereka di antara banyak pilihan yang tersedia.Tujuan penelitian ini adalah untuk membuat aplikasi sistem rekomendasi tempat wisata di Jakarta yang menggunakan metode Collaborative Filltering. Penelitian ini akan mengembangkan sistem rekomendasi tempat wisata di Jakarta yang menggunakan collaborative filltering dengan menggunakan bahasa pemograman Pyhton dan Wxpyhton sebagai framework Grapichal User interfaceI (GUI) menggunakan aplikasi Pycharm. Dataset yang terdiri dari 985 data telah melewati tahap pree-processing. Hasil evaluasi model menunjukkan bahwa nilai Mean Squared Error (MAE) adalah 0.7561 dan nilai Root Mean Squared Error (RMSE) adalah 1.0634. Ini menunjukkan bahwa akurasi sistem rekomendasi adalah sekitar 91.60% berdasarkan Mean Squared Error (MAE) dan 88.18% berdasarkan Root Mean Squared Error (RMSE).Kata kunci: Sistem Rekomendasi; Collaborative Filtering; Jakarta; Tempat Wisata; Pycharm.
Implementasi Metode K-Nearest Neighbor Pada Sentimen Masyarakat Terkait Pelaksanaan KTT G20 Lase, Anan Sosmita; Berutu, Sunneng Sandino; Budiati, Haeni
Progresif: Jurnal Ilmiah Komputer Vol 19, No 2: Agustus 2023
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v19i2.1236

Abstract

Indonesia is the first Asian country to be appointed to lead the 20th global summit (G20 Summit) and it will be held in Bali, Indonesia. The successful implementation of the G20 Summit attracted public attention, triggering various kinds of public sentiment (opinion) regarding the G20 Summit. With public sentiment, the government can find out the views or opinions of the public regarding the G20 Summit that has been held. Public sentiment can be in the form of positive sentiment or negative sentiment, for this reason a process of classification and analysis of these sentiments is needed. The process of classifying public sentiment starts from the problem identification process, then crawling data to retrieve Tweets from Twitter, data preprocessing to process data, data labeling, KNN-based model training, classification model testing and evaluation. The results of the sentiment analysis show the results of the accuracy of 97.75% accuracy, 100% precision and 97.71% recall.Keywords: Sentiment analysis; K-Nearest Neighbor, G20 summit implementation AbstrakIndonesia merupakan negara Asia pertama yang di tunjuk untuk memimpin Konferensi Tingkat Tinggi Global 20 (KTT G20) dan dilaksanakan di bali, Indonesia. Terlaksananya KTT G20 sukses menarik perhatian publik sehingga memicu munculnya berbagai macam sentimen (opini) masyarakat mengenai KTT G20. Dengan adanya sentimen masyarakat pemerintah dapat mengetahui bagaimana pandangan ataupun opini masyarakat terkait KTT G20 yang telah di laksanakan. Sentimen masyarakat dapat berupa sentimen positif ataupun sentimen negatif, untuk itu di perlukan proses klasifikasi dan analisis terhadap sentimen tersebut. Proses pengklasifikasian sentimen masyarakat di mulai dari proses identifikasi masalah, lalu crawling data untuk mengambil tweet dari Twitter, preprocessing data untuk mengolah data, pelabelan data, training model berbasis KNN, uji model klasifikasi dan evaluasi. Hasil analisis sentimen menunjukkan tingkat akurasi 97,75%, presisi 100% dan recall 97,71%.Kata kunci: Analisis sentimen; K-Nearest Neighbor; Pelaksanaan KTT G20
PENERAPAN METODE DECISION TREE PADA SENTIMEN MEDIA SOSIAL TERKAIT KOMISI PEMILIHAN UMUM (KPU) SEBELUM DAN SESUDAH PILPRES 2024 Veren, Yolanda; Berutu, Sunneng Sandino; Budiati, Haeni
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 9, No 4 (2024)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v9i4.5532

Abstract

Pemilihan Presiden 2024 di Indonesia menandai momen krusial dalam dinamika politik negara. Penelitian ini mengamati perubahan pandangan masyarakat terhadap Komisi Pemilihan Umum (KPU) sebelum dan setelah Pilpres 2024 dengan menerapkan metode klasifikasi Decision Tree. Dataset yang digunakan terdiri dari 5794 (tambahin tanggal) tweet yang telah melewati serangkaian tahap, mulai dari crawling, pre processing data, terjemahan, pelabelan, klasifikasi, hingga evaluasi model. Hasil analisis menunjukkan bahwa sebelum Pilpres 2024, sentimen positif mencapai 39.98%, sentimen netral sebesar 38.39%, dan sentimen negatif sebesar 21.63%. Setelah Pilpres 2024, terjadi peningkatan dalam sentimen positif menjadi 45.45%, sedangkan sentimen netral mengalami penurunan menjadi 36.67%, dan sentimen negatif juga mengalami penurunan menjadi 17.87%. Berdasarkan dataset Komisi Pemilihan Umum (KPU) setelah Pilpres 2024 akurasi prediksi meningkat menjadi 67, dengan sentimen netral memiliki presisi sebesar 70. Sedangkan berdasarkan dataset pada Komisi Pemilihan Umum (KPU) setelah Pilpres 2024, recall untuk sentimen positif adalah 69, sementara f1-score untuk sentimen netral adalah 72.
ANALISIS PERBINCANGAN DALAM GRUP WHATSAPP DENGAN K-MEANS CLUSTERING Tri Elsa, Maria; Berutu, Sunneng Sandino; Maedjaja, Febe
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 9, No 4 (2024)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v9i4.5554

Abstract

Whatsapp merupakan sebuah aplikasi yang mempermudah pengguna untuk mengirim pesan teks, foto, video, melakukan panggilan suara, dan panggilan video secara gratis dengan koneksi internet. “Grup Whatsapp Jual Beli Area UKRIM” memiliki sekitar 26.000 arsip pesan, yang membuktikan tingginya aktivitas dan interaksi anggota dalam transaksi jual beli. Anggota grup akan terbantu membuat strategi promosi barang jika mereka dapat memanfaatkan informasi mengenai kata dan barang yag paling sering muncul dalam chat group. Untuk mendapatkan informasi mengenai kata dan barang yang paling sering muncul dalam chat group diperlukan text clustering. Penelitian ini menggunakan Metode K-Means dalam melakukan text clustering untuk memperoleh kata-kata yang sering muncul tersebut. Setelah melalui prosedur pra-pemrosesan teks dan penerapan Metode Elbow, jumlah data diperkecil menjadi 4.732 data dan ditentukan 10 cluster yang optimal dalam “Grup Whatsapp Jual Beli Area UKRIM”. Hasil penerapan K-Means memperlihatkan kata yang paling sering muncul dalam cluster 0 adalah kata “jual” , dalam cluster 1 kata “info”, dalam cluster 2  kata “beli”, dalam cluster 3 kata “japri”, dalam cluster 4 kata “gas”, dalam cluster 5 kata “motor”, dalam cluster 6 kata “info”,”kost”, dalam cluster 7  kata  “hp”, dalam cluster  8  kata “kucing”,  dan dalam cluster 9  kata “rak”. Secara menyeluruh kata yang paling banyak muncul adalah kata “info” dengan jumlah 910 dan kata yang paling sedikit muncul adalah kata “iphone” dan “hewan”. Barang dagangan yang sering muncul adalah “meja”, “kipas”, “lemari”, “kasur”, dan “laptop
Co-Authors Bobby Girsang Chen, Yeong-Chin Dachi, Christian Moris Febe Maedjaja Febe Maedjaja Fornieli Gulo Haeni Budiati Heri Wijayanto Indriyani Hulu Jatmika Jatmika Jatmika Jatmika Jatmika Jatmika, Jatmika Jatmitka, Jatmitka Juli Supriyanto Gea Kambayo, Andreas Noprianto Kambayo Kristian Juri Damai Lase Laia, Yardiana Lase, Anan Sosmita Nauli, Sumitta Nehe, Pius Hermanto Nshimiyimana, Aristophane Rahman, Mosiur Reonardh Sibarani Sispianygala, Aprilia Sumihar, Yo’el Pieter Tri Elsa, Maria Tri Yuli Yanto Veren, Yolanda Yuan, Anita Zakaria, Immanuel Richie De Harjo Zendrato, Agus Dirgahayu