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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) TELKOMNIKA (Telecommunication Computing Electronics and Control) SITEKIN: Jurnal Sains, Teknologi dan Industri JUSIFO : Jurnal Sistem Informasi Jurnal Ilmiah Rekayasa dan Manajemen Sistem Informasi Sistemasi: Jurnal Sistem Informasi JURNAL MEDIA INFORMATIKA BUDIDARMA Seminar Nasional Teknologi Informasi Komunikasi dan Industri INOVTEK Polbeng - Seri Informatika JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Inotera JURIKOM (Jurnal Riset Komputer) Jurnal Sistem Cerdas JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Building of Informatics, Technology and Science Jurnal Sistem Informasi dan Informatika (SIMIKA) Zonasi: Jurnal Sistem Informasi Indonesian Journal of Electrical Engineering and Computer Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Sistem Komputer dan Informatika (JSON) Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Teknik Informatika (JUTIF) Jurnal Pendidikan dan Teknologi Indonesia Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Indonesian Journal of Informatic Research and Software Engineering Malcom: Indonesian Journal of Machine Learning and Computer Science Journal of Informatics, Electrical and Electronics Engineering Jurnal Penelitian Teknologi Informasi dan Sains The Indonesian Journal of Computer Science INOVTEK Polbeng - Seri Informatika
Febi Nur Salisah
Program Studi Sistem Informasi, Fakultas Sains Dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau
Aerospace Engineering Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Mechanical Engineering Other

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Journal : building of informatics technology and science

 1 
Perbandingan Performa Algoritma NBC, C4.5, dan KNN dalam Analisis Sentimen Masyarakat terhadap Krisis Petani Muda pada Media Sosial Facebook Nurkholis, Nurkholis; Permana, Inggih; Salisah, Febi Nur; Mustakim, Mustakim; Afdal, M
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.6082

Abstract

In Indonesia, young farmers face various challenges and crises that hinder the growth and sustainability of the agricultural sector. They face obstacles such as lack of access to capital, limited technology, climate change, and low selling prices for their crops. In addition, they also often face problems in obtaining accurate and relevant information in an effort to facilitate better decision-making in agricultural businesses, so that the interest of young people today to become farmers is decreasing. The study aims to Compare the Performance of NBC, C4.5, and KNN Algorithms in the Analysis of Public Sentiment towards the Young Farmer Crisis on Facebook Social Media. The application of the K-Fold Cross Validation method is (K = 10). Sentiment analysis is carried out with 3 labels (positive, negative, and neutral). The data used in making the classification model (data from preprocessing the stemming column) using (Google Colab) amounted to 4,878 data with Positive sentiment of 43.13% (2,104), Neutral 39.59% (1,931), Negative 17.28% (843) from the initial data without nested comments, which is 4,981 and the total number of Facebook data is 2,900 likes, 6,700 comments, and 3.3 million viewers. The accuracy of the NBC algorithm is 57.32%, the C4.5 algorithm is 98.42%, and the KNN algorithm (K = 19) is 97.33%. It can be concluded that the results of the comparison of the performance of the three algorithms using (Rapidminer10.3), the C4.5 algorithm gets a higher accuracy of 98.42% and is superior because it produces a decision tree.
Density-Based Spatial Clustering, K-Means and Frequent Pattern Growth for Clustering and Association of Malay Cultural Text Data in Indonesia Mustakim, Mustakim; Salisah, Febi Nur
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7512

Abstract

Several studies state the need to develop information technology to disseminate information related to culture in Indonesia. There are many similar studies but they still have weaknesses, one of which is that they do not use machine learning and intelligent computing. This research answers the challenges of previous researchers, namely developing machine learning-based learning applications using the Density-Based Spatial Clustering of Application Noise (DBSCAN) and Frequent Pattern Growth (FP-Growth) algorithms. The results of the modeling of the two algorithms are deemed to still require improvement in the future, as it is proven that DBSCAN does not yet have optimal validity. So in this research, one of the comparison algorithms is used, namely K-Means Clustering, with a better evaluation than DBSCAN. The modeling results were implemented into mobile programming as a cultural learning application in Indonesia, especially Riau Malay Culture, the black box testing results had an accuracy of 100% and the User Acceptance Test (UAT) was 86%. Thus, it is concluded that this application can be used effectively and efficiently for general users.
Perbandingan Performa Algoritma SVR, LSTM, dan SARIMA dalam Peramalan Produksi Kelapa Sawit Hendri, Desvita; Permana, Inggih; Salisah, Febi Nur; Afdal, M; Megawati, Megawati; Saputra, Eki
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7170

Abstract

Oil palm production in Indonesia fluctuates significantly due to various factors such as weather, soil fertility, and fruit bunch condition. These changes These changes have an impact on price stability, supply and planning for the palm oil industry. industry planning. Therefore, to improve decision-making in this industry, an accurate forecasting method is required to improve decision-making regarding distribution. appropriate decision-making regarding distribution. This study aims to compare the performance of three machine learning-based forecasting methods, namely Support Vector Regression (SVR), Long Short-Term Memory (LSTM), and Seasonal Autoregressive Integrated Moving Average (SARIMA), in predicting palm oil production based on historical data for the last 10 years obtained from PTPN V Riau. The evaluation results show that the SVR model with a linear kernel provides the best performance with an MSE value of 4.1718. with MSE 4.1718, RMSE 0.0020, MAE 0.0018, MAPE 0.2014% and R2 0.9988. The SVR model provides superior prediction results compared to LSTM and SARIMA. with LSTM and SARIMA in forecasting palm oil production. This research is expected to make a real contribution in the development of a more reliable prediction system, thus supporting operational efficiency and stability of the palm oil industry in Indonesia. stability of the palm oil industry in Indonesia.
Perbandingan Algoritma LSTM, Bi-LSTM, GRU, dan Bi-GRU untuk Prediksi Harga Saham Berbasis Deep Learning Tshamaroh, Muthia; Permana, Inggih; Salisah, Febi Nur; Muttakin, Fitriani; Afdal, M
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7252

Abstract

Stock price prediction is an important component in making investment decisions. This study aims to compare the performance of four deep learning models, namely LSTM, Bi-LSTM, GRU, and Bi-GRU, in predicting stock prices, in order to find the most optimal model for the implementation of an accurate stock price prediction system. Five years of historical data undergoes normalization, windowing, and is separated into training data, validation data, and test data. Model training is conducted with different settings of batch size, timestep, and three kinds of optimizers (Adam, SGD, RMSprop). Performance assessment employs MSE, RMSE, MAE, and R² measurements. The findings indicate that the Bi-GRU model utilizing Adam optimizer settings, a batch size of 8, and a timestep of 21 yields the highest performance, achieving an MSE of 0.0003, an RMSE of 0.0169, an MAE of 0.0129, and an R² of 0.9438. This model demonstrates a strong capability to identify intricate patterns and long-term temporal relationships, outperforming other models in accuracy. The results advocate for the establishment of a predictive system that aids investors and firms in making strategic decisions based on data.
Analisis Sentimen Masyarakat Terhadap Kebijakan IKN Pada Periode Jokowi dan Prabowo Menggunakan Algoritma NBC, SVM, dan K-NN Nasution, Nur Shabrina; Permana, Inggih; Salisah, Febi Nur; Afdal, M; Megawati, Megawati
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7276

Abstract

The relocation of the National Capital City (IKN) from Jakarta to East Kalimantan has generated a variety of responses from the Indonesian people recorded through social media, especially platform X. This study aims to analyze and compare public sentiment towards the IKN policy in two periods of government, namely President Joko Widodo and President Prabowo Subianto. This study aims to analyze and compare public sentiment towards the policy of the National Capital City during two periods of government, namely President Joko Widodo and President Prabowo Subianto, using a machine learning approach. The three algorithms used in sentiment classification are Naive Bayes Classifier (NBC), Support Vector Machine (SVM), and K-Nearest Neighbor (K-NN). The research process includes data crawling (600 data each per period), text preprocessing (cleaning, tokenizing, filtering, stemming), data labeling using Lexicon-Based approach with InSet dictionary, and weighting using TF-IDF method. The results of the analysis show that in the Jokowi period, public sentiment tends to be more balanced, with the dominance of negative sentiment (35.9%), followed by positive sentiment (33.4%) and neutral (30.7%). Whereas in the Prabowo period, negative sentiment increased to 40.3%, while positive decreased to 26.3%. Based on the model accuracy evaluation, in the Jokowi period, the NBC algorithm showed the best performance with an accuracy of 73%, while in the Prabowo period, the SVM algorithm excelled with the highest accuracy reaching 81%. These findings provide a dynamic picture of public perception of IKN policies under two different governments.
Klasifikasi Text Dokumen Web Berbasis Supervised Learning Sebagai Pemodelan Aplikasi Pembelajaran Kebudayaan Melayu di Indonesia Mustakim, Mustakim; Salisah, Febi Nur; Suryani, Suryani
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8499

Abstract

Indonesia, as the largest archipelagic country, is home to diverse cultures, including Malay culture in Riau Province. The website features numerous text documents, including articles, news, and personal documents, uploaded by members of the cultural community. This study aims to support the preservation of Malay culture through technology by implementing a digital learning system based on Machine Learning. Previous research has identified weaknesses in the application of intelligent systems and machine learning algorithms. This study tests five classification algorithms Random Forest, SVM, Naïve Bayes, KNN, and PNN to improve the system's accuracy and performance. The results show that Random Forest achieved the highest accuracy of 91.17%, followed by KNN at 88.23%, SVM and NBC at 82.35%, and PNN at 76.47%. The developed Digital Learning System (DLS) received positive feedback, with a User Acceptance Test (UAT) score of 86% and a 100% success rate in Blackbox testing, demonstrating stable performance across various devices. This research introduces a new innovation in Malay cultural preservation applications, utilizing Machine Learning algorithms to enhance both accuracy and functionality.
Co-Authors A Anggraini Afdal Muhammad Efendi Ahsyar, Tengku Khairil Anggi Widya Atma Nugraha Anggia Anfina Anggy Julia Wulandari Angraini Angraini Anisa Nirmala, Fitri Anwar, Tengku Khairil Arabiatul Adawiyah Arif Marsal Arif Marsal Arif Marsal Arrazak, Fadlan Bayu Putra Danil Risaldi Darmawan, Reza Dewi Astuti Efendi, Harisman Eki Saputra Eki Saputra Eki Saputra Elin Haerani Endah Purnamasari Esis Srikanti Fachrurozi Fadhilah Syafria Fadil Rahmat Andini Febrian, Dany Fernanda, Ustara Dwi Fiki Fitri Wulandari Fitriah, Ma’idatul Fitriah, Ma’idatul Fitriani Muttakin Fitriani Muttakin Giansyah, Qhoiril Aldi Gustinov, Mhd Dion Hasbi Sidiq Arfajsyah Hendri, Desvita Husaini, Fahri Idria Maita Idria Maita Idria Maita Idriani R, Nova Imam Muttaqin Indah Lestari Indri Dian Pertiwi Inggih Permana Intan, Sofia Fulvi Jayadi, Puguh Jazman , Muhammad Jazman, Muhammad Kusuma, Gathot Hanyokro Leony Lidya M Afdal M Afdal M. Afdal M. Afdal M. Afdal M.Afdal Maheri, Rahman Maulana, Rizki Azli Mawaddah, Zuriatul Mega wati, Mega Megawati Megawati - Megawati Megawati Mona Fronita Mubarak MR, Najmuddin Muhammad Afdal Muhammad Iqbal Indrawan Muhammad Jazman Muhammad Jazman Muhammad Luthfi Muhammad Luthfi Hamzah Muhammad Munawir Arpan Munzir, Medyantiwi Rahmawita Mustakim Mustakim Muttakin, Fitriani Nabila Putri Nailul Amani Nardialis Nardialis Nasution, Nur Shabrina Naufal Fikri, R. Adlian Nesdi Evrilyan Rozanda Nesdi Evrilyan Rozanda Norhavina Norhavina Nuraisyah Nuraisyah Nurkholis Nurkholis Nurrahma, Intan Puput Iswandi Putra, Adhytia Pratama Putri, Amanda Iksanul Rahma Aliya Rahma Devi Rahmawita M, Medyantiwi Rahmawita, Medyantiwi Rangga Arief Putra Ria Agustina Rice Novita Rice Novita Rizka Fitri Yansi Rizki Pratama Putra Agri Rozanda, Nesdi Evrilyan Sanusi Saputri, Setia Ningsih Sari, Gusmelia Puspita Sarjon Defit Setiawati, Elsa Shir Li Wang Shulhan Abdul Gofar Siti Zainah Sulthan Habib Suryani Suryani Susilawati Susilawati Syahri, Alfi Syaifullah Syaifullah Syaifullah Syaifullah Syaifullah Syaifullah Syarif, Yulia Tengku Khairil Ahsyar Tengku Khairil Ahsyar Tengku Khairil Ahsyar Tengku Khairil Ahsyar Tshamaroh, Muthia Uci Indah Sari Winda Wahyuti Wira Mulia, M. Roid Zarnelly Zarry, Cindy Kirana