Pub Date : 2023-10-01DOI: 10.24002/jbi.v14i02.7244
None Vidya Chandradev, None I Made Agus Dwi Suarjaya, None I Putu Agung Bayupati
The COVID-19 pandemic has resulted in declining tourism visits and hotel occupancy. Hoteliers must monitor visitor lifestyles to sustain their businesses. One way to achieve this is by understanding the sentiment of hotel visitors through review analysis, enabling better decision-making regarding service and business aspects in the hotel industry. This research applies the natural language processing deep learning model BERT to analyze positive and negative sentiments from hotel visitor reviews in Indonesia. The BERT model undergoes a pre-trained and fine-tuned process to produce accurate sentiment analysis. Evaluation results demonstrate that the fine-tuned SmallBERT model performs well, trained on a dataset of 515k hotel reviews for five epochs. The SmallBERT model achieves an accuracy of 91.40%, precision of 90.51%, recall of 90.51%, and an F1 score of 90.51% when evaluated with manually labelled datasets. Visualizations of the predominantly positive sentiment comparisons are conducted using Tableau.
{"title":"Analisis Sentimen Review Hotel Menggunakan Metode Deep Learning BERT","authors":"None Vidya Chandradev, None I Made Agus Dwi Suarjaya, None I Putu Agung Bayupati","doi":"10.24002/jbi.v14i02.7244","DOIUrl":"https://doi.org/10.24002/jbi.v14i02.7244","url":null,"abstract":"The COVID-19 pandemic has resulted in declining tourism visits and hotel occupancy. Hoteliers must monitor visitor lifestyles to sustain their businesses. One way to achieve this is by understanding the sentiment of hotel visitors through review analysis, enabling better decision-making regarding service and business aspects in the hotel industry. This research applies the natural language processing deep learning model BERT to analyze positive and negative sentiments from hotel visitor reviews in Indonesia. The BERT model undergoes a pre-trained and fine-tuned process to produce accurate sentiment analysis. Evaluation results demonstrate that the fine-tuned SmallBERT model performs well, trained on a dataset of 515k hotel reviews for five epochs. The SmallBERT model achieves an accuracy of 91.40%, precision of 90.51%, recall of 90.51%, and an F1 score of 90.51% when evaluated with manually labelled datasets. Visualizations of the predominantly positive sentiment comparisons are conducted using Tableau.","PeriodicalId":499081,"journal":{"name":"Jurnal Buana Informatika","volume":"2667 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135761880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Awan Cumulonimbus (Cb) merupakan awan yang berbahaya bagi banyak aktivitas manusia. Untuk mengurangi efek tersebut diperlukan sistem untuk mengklasifikasikan pembentukannya. Pembentukan awan Cb dapat dilihat pada citra Himawari-8 IR. Tujuan penelitian ini adalah membuat sistem klasifikasi formasi awan Cb dengan citra Himawari-8 IR Enhanced menggunakan metode CNN model GoogleNet. Total data yang akan digunakan sebanyak 2.026 data citra. Pengujian parameter dilakukan pada model CNN GoogleNet pada penelitian ini yaitu rasio sebaran data 90:10 dan 80:20. Probabilitas drop out 0,6; 0,7; dan 0,8. dan batch size 8, 16, 32, dan 64. Uji coba yang dilakukan pada penelitian ini menghasilkan nilai sensitivitas 100,00%, akurasi 99,00%, dan spesifisitas 99,60% yang diperoleh dari distribusi data eksperimen sebesar 90:10, probabilitas 0,8 dan ukuran batch 8.
{"title":"Classification of Cumulonimbus Cloud Formation based on Himawari Images using Convolutional Neural Network model Googlenet","authors":"Mohammad Rizal Abidin, None Dian candra Rini Novitasari, None Hani Khaulasari, None Fajar Setiawan","doi":"10.24002/jbi.v14i02.7417","DOIUrl":"https://doi.org/10.24002/jbi.v14i02.7417","url":null,"abstract":"Awan Cumulonimbus (Cb) merupakan awan yang berbahaya bagi banyak aktivitas manusia. Untuk mengurangi efek tersebut diperlukan sistem untuk mengklasifikasikan pembentukannya. Pembentukan awan Cb dapat dilihat pada citra Himawari-8 IR. Tujuan penelitian ini adalah membuat sistem klasifikasi formasi awan Cb dengan citra Himawari-8 IR Enhanced menggunakan metode CNN model GoogleNet. Total data yang akan digunakan sebanyak 2.026 data citra. Pengujian parameter dilakukan pada model CNN GoogleNet pada penelitian ini yaitu rasio sebaran data 90:10 dan 80:20. Probabilitas drop out 0,6; 0,7; dan 0,8. dan batch size 8, 16, 32, dan 64. Uji coba yang dilakukan pada penelitian ini menghasilkan nilai sensitivitas 100,00%, akurasi 99,00%, dan spesifisitas 99,60% yang diperoleh dari distribusi data eksperimen sebesar 90:10, probabilitas 0,8 dan ukuran batch 8.","PeriodicalId":499081,"journal":{"name":"Jurnal Buana Informatika","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135761881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-01DOI: 10.24002/jbi.v14i02.6977
Stephanie Pamela Adithama, B. Yudi Dwiandiyanta, Sevia Berliana Wiadji
Identification of Batik in Central Java using Transfer Learning Method. Batik was recognized as a human heritage for oral and nonmaterial culture by UNESCO due to its symbolic and philosophical ties to the lives of Indonesians. However, the younger generation is gradually losing itslegacy because of technological and sociological changes that have influenced Indonesian batik. Consequently, batik knowledge is disappearing. A convolutional neural network and transfer learning techniques were utilized in deep learning to construct a model recognising batik motifs. The study utilized a dataset of one thousand images, five classes of batik designs (Banji, Kawung, Slope, Parang, and Slobog), and pre-trained architectural models VGG16 and VGG19 on Keras. The best model utilizes the VGG16 architecture, and the number of epochs is 50,with the result of testing accuracy of 0.9200.
{"title":"Identification of Batik in Central Java using Transfer Learning Method","authors":"Stephanie Pamela Adithama, B. Yudi Dwiandiyanta, Sevia Berliana Wiadji","doi":"10.24002/jbi.v14i02.6977","DOIUrl":"https://doi.org/10.24002/jbi.v14i02.6977","url":null,"abstract":"Identification of Batik in Central Java using Transfer Learning Method. Batik was recognized as a human heritage for oral and nonmaterial culture by UNESCO due to its symbolic and philosophical ties to the lives of Indonesians. However, the younger generation is gradually losing itslegacy because of technological and sociological changes that have influenced Indonesian batik. Consequently, batik knowledge is disappearing. A convolutional neural network and transfer learning techniques were utilized in deep learning to construct a model recognising batik motifs. The study utilized a dataset of one thousand images, five classes of batik designs (Banji, Kawung, Slope, Parang, and Slobog), and pre-trained architectural models VGG16 and VGG19 on Keras. The best model utilizes the VGG16 architecture, and the number of epochs is 50,with the result of testing accuracy of 0.9200.","PeriodicalId":499081,"journal":{"name":"Jurnal Buana Informatika","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135762620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-01DOI: 10.24002/jbi.v14i02.7039
None Sri Rahayu, None Jajang Jaya Purnama, None Abdul Hamid, None Nina Kurnia Hikmawati
Peran artificial intelligence memudahkan mencari informasi yang tepat dan akurat bahkan penyelesaian masalah dengan model yang kompleks. Salah satu terobosan berbasis AI adalah ChatGPT oleh OpenAI pada tahun 2020, dilanjutkan dengan versi terbaru pada tahun 2023 yaitu GPT–3. Sejak saat itu, beberapa teknologi AI serupa versi mobile mulai bermunculan, salah satunya AicoGPT. Namun, kinerja dari aplikasi serupa ini belum dapat diandalkan sehingga masih perlu menganalisis tanggapan para penggunanya, apakah akan sama menakjubkannya atau tidak. Dari permasalahan tersebut, penelitian ini dibuat dengan tujuan untuk menganalisis 1443 data ulasan para pengguna aplikasi AicoGPT di Google Playstore dengan teknik analisis sentimen menggunakan TFIDF dan perbandingan klasifikasi LR dan SVM. Dari kedua ujicoba tersebut, menghasilkan akurasi terbaik dengan Algoritma SVM, yaitu sebesar 92%. Sedangkan LR menghasilkan akurasi sebesar 89%. Dari penelitian ini, dapat disimpulkan secara singkat bahwa metode TF-IDF dengan klasifikasi SVM, cocok digunakan untuk melakukan analisis sentimen dari dataset yang diteliti.
{"title":"Analisis Sentimen AicoGPT (Generative Pre-trained Transformer) Menggunakan TF-IDF","authors":"None Sri Rahayu, None Jajang Jaya Purnama, None Abdul Hamid, None Nina Kurnia Hikmawati","doi":"10.24002/jbi.v14i02.7039","DOIUrl":"https://doi.org/10.24002/jbi.v14i02.7039","url":null,"abstract":"Peran artificial intelligence memudahkan mencari informasi yang tepat dan akurat bahkan penyelesaian masalah dengan model yang kompleks. Salah satu terobosan berbasis AI adalah ChatGPT oleh OpenAI pada tahun 2020, dilanjutkan dengan versi terbaru pada tahun 2023 yaitu GPT–3. Sejak saat itu, beberapa teknologi AI serupa versi mobile mulai bermunculan, salah satunya AicoGPT. Namun, kinerja dari aplikasi serupa ini belum dapat diandalkan sehingga masih perlu menganalisis tanggapan para penggunanya, apakah akan sama menakjubkannya atau tidak. Dari permasalahan tersebut, penelitian ini dibuat dengan tujuan untuk menganalisis 1443 data ulasan para pengguna aplikasi AicoGPT di Google Playstore dengan teknik analisis sentimen menggunakan TFIDF dan perbandingan klasifikasi LR dan SVM. Dari kedua ujicoba tersebut, menghasilkan akurasi terbaik dengan Algoritma SVM, yaitu sebesar 92%. Sedangkan LR menghasilkan akurasi sebesar 89%. Dari penelitian ini, dapat disimpulkan secara singkat bahwa metode TF-IDF dengan klasifikasi SVM, cocok digunakan untuk melakukan analisis sentimen dari dataset yang diteliti.","PeriodicalId":499081,"journal":{"name":"Jurnal Buana Informatika","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136054798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-01DOI: 10.24002/jbi.v14i02.5027
None Findra Kartika Sari Dewi, None Thomas Adi Purnomo Sidhi, Yonathan Christofer Darmawan
User interface dan user experience berperan penting dalam pengembangan aplikasi, yang menjadi tolok ukur keberhasilan memenuhi kebutuhan pengguna. Perlu dilakukan pengukuran usability, pencarian permasalahan dan rekomendasi perbaikan antarmuka web SIATMA, menggunakan metode Heuristic Evaluation (HE), dan System Usability Scale (SUS). Pengambilan data HE menggunakan daftar cek evaluasi yang diisi oleh evaluator dan SUS menggunakan kuesioner yang diisi oleh mahasiswa Universitas Atma Jaya Yogyakarta. Menggunakan HE ditemukan 25 permasalahan usability dengan jumlah terbanyak pada Visibility of System Status dan Aesthetic and Minimalist Design. Permasalahan tersebut terdiri dari 10 masalah cosmetic, lima masalah minor dengan, delapan masalah major, dan dua masalah catastrophe. Diberikan 25 solusi perbaikan yang direkomendasikan oleh evaluator, sedangkan menggunakan SUS dihasilkan skor SUS sebesar 54,4. Kedua hasil tersebut menunjukkan bahwa SIATMA belum memuaskan dari segi usability dan perlu dilakukan perbaikan, seperti memberikan detail minor seperti icon, peringatan sampai dengan perbaikan layout dan menambahkan halaman baru.
{"title":"Analisis Usability Web SIATMA dengan Metode Heuristic Evaluation dan System Usability Scale","authors":"None Findra Kartika Sari Dewi, None Thomas Adi Purnomo Sidhi, Yonathan Christofer Darmawan","doi":"10.24002/jbi.v14i02.5027","DOIUrl":"https://doi.org/10.24002/jbi.v14i02.5027","url":null,"abstract":"User interface dan user experience berperan penting dalam pengembangan aplikasi, yang menjadi tolok ukur keberhasilan memenuhi kebutuhan pengguna. Perlu dilakukan pengukuran usability, pencarian permasalahan dan rekomendasi perbaikan antarmuka web SIATMA, menggunakan metode Heuristic Evaluation (HE), dan System Usability Scale (SUS). Pengambilan data HE menggunakan daftar cek evaluasi yang diisi oleh evaluator dan SUS menggunakan kuesioner yang diisi oleh mahasiswa Universitas Atma Jaya Yogyakarta. Menggunakan HE ditemukan 25 permasalahan usability dengan jumlah terbanyak pada Visibility of System Status dan Aesthetic and Minimalist Design. Permasalahan tersebut terdiri dari 10 masalah cosmetic, lima masalah minor dengan, delapan masalah major, dan dua masalah catastrophe. Diberikan 25 solusi perbaikan yang direkomendasikan oleh evaluator, sedangkan menggunakan SUS dihasilkan skor SUS sebesar 54,4. Kedua hasil tersebut menunjukkan bahwa SIATMA belum memuaskan dari segi usability dan perlu dilakukan perbaikan, seperti memberikan detail minor seperti icon, peringatan sampai dengan perbaikan layout dan menambahkan halaman baru.","PeriodicalId":499081,"journal":{"name":"Jurnal Buana Informatika","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135761890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
KTP memiliki banyak fungsi, seperti sebagai kartu identitas, dalam proses pendaftaran, dalam proses kepengurusan, serta untuk mengakses layanan. Hingga saat ini, pendaftaran KTP dilakukan dengan diketik sehingga tidak hemat waktu dan tenaga, serta sering menyebabkan kesalahan dalam pengetikan sehingga data yang disimpan tidak sesuai. Oleh karena itu, dikembangkan aplikasi dengan fitur otomatisasi pengisian data KTPmenggunakan OCR. Metode OCRyang akan digunakan ditentukan dengan pengujian akurasi metode Pytesseract dan template matching pada kondisi menggunakan kamera smartphone dengan pencahayaan gelap, terang, terang sekali, dan menggunakan kamera laptop. Rata-rata tingkat akurasi dari empat pengkondisian yang didapatkan oleh metode Pytesseract adalah 98,33%, sedangkan rata-rata yang didapatkan oleh metode template matching adalah 67,33%. Berdasarkan hasil ini, sistem OCR yang dikembangkan menggunakan metode Pytesseract. ID Cards serve several purposes, including identification, registration, management, and accessing public services. Until now, ID Cards registration has been done by typing so it takes more time and effort, and often causes errors in typing so that the data stored does not match. Therefore, an application was developed with OCR-based automation for inputting ID card information. The method used for OCR is determined by testing the accuracy of Pytesseract and Template Matching in condition using a smartphone camera with dark, bright, and very bright lighting, and using a laptop camera. The average of the accuracy of the four conditions obtained by Pytesseract is 98.33%, while the average obtained by Template Matching is 67.33%. Based on these results, the OCR system developed using Pytesseract.
{"title":"Perbandingan Pytesseract dan Template Matching Untuk Otomatisasi Input Data KTP","authors":"Teresa Octaviani, Hendry Setiawan, Oesman Hendra Kelana","doi":"10.24002/jbi.v14i02.7612","DOIUrl":"https://doi.org/10.24002/jbi.v14i02.7612","url":null,"abstract":"KTP memiliki banyak fungsi, seperti sebagai kartu identitas, dalam proses pendaftaran, dalam proses kepengurusan, serta untuk mengakses layanan. Hingga saat ini, pendaftaran KTP dilakukan dengan diketik sehingga tidak hemat waktu dan tenaga, serta sering menyebabkan kesalahan dalam pengetikan sehingga data yang disimpan tidak sesuai. Oleh karena itu, dikembangkan aplikasi dengan fitur otomatisasi pengisian data KTPmenggunakan OCR. Metode OCRyang akan digunakan ditentukan dengan pengujian akurasi metode Pytesseract dan template matching pada kondisi menggunakan kamera smartphone dengan pencahayaan gelap, terang, terang sekali, dan menggunakan kamera laptop. Rata-rata tingkat akurasi dari empat pengkondisian yang didapatkan oleh metode Pytesseract adalah 98,33%, sedangkan rata-rata yang didapatkan oleh metode template matching adalah 67,33%. Berdasarkan hasil ini, sistem OCR yang dikembangkan menggunakan metode Pytesseract. ID Cards serve several purposes, including identification, registration, management, and accessing public services. Until now, ID Cards registration has been done by typing so it takes more time and effort, and often causes errors in typing so that the data stored does not match. Therefore, an application was developed with OCR-based automation for inputting ID card information. The method used for OCR is determined by testing the accuracy of Pytesseract and Template Matching in condition using a smartphone camera with dark, bright, and very bright lighting, and using a laptop camera. The average of the accuracy of the four conditions obtained by Pytesseract is 98.33%, while the average obtained by Template Matching is 67.33%. Based on these results, the OCR system developed using Pytesseract.","PeriodicalId":499081,"journal":{"name":"Jurnal Buana Informatika","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135761878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In today's world, there is a high demand for accurate techniques to classify emotions in various fields. This study proposed utilizing a Convolutional Neural Network (CNN) optimized with a Hyperband Tuner (HT) to perform the Emotion Classification task in the Indonesian language effectively. Various feature extraction techniques experiments were conducted to explore the best combinations of feature extraction and CNN for the data set, including CountVectorizer (CV), TF-IDF, and Keras Tokenizer (KT). Last, the proposed methodology was evaluated and compared to the stateof-the-art techniques, including K-Nearest Neighbors (KNN), Decision Tree (DT), Naive Bayes (NB), and Boosting SVM. The experimental results revealed that the proposed method in this research outperforms the existing technique as evidenced by the accuracy, precision, recall, and F1-score metrics, which respectively reached 71.5655%, 71.5483%, 71.5655%, and 71.0041%.
{"title":"Emotion Classification in Indonesian Language: A CNN Approach with Hyperband Tuning","authors":"Muhammad Yeza Baihaqi, Edmun Halawa, Riri Asyahira Sariati Syah, Anniza Nurrahma, Wilbert Wijaya","doi":"10.24002/jbi.v14i02.7558","DOIUrl":"https://doi.org/10.24002/jbi.v14i02.7558","url":null,"abstract":"In today's world, there is a high demand for accurate techniques to classify emotions in various fields. This study proposed utilizing a Convolutional Neural Network (CNN) optimized with a Hyperband Tuner (HT) to perform the Emotion Classification task in the Indonesian language effectively. Various feature extraction techniques experiments were conducted to explore the best combinations of feature extraction and CNN for the data set, including CountVectorizer (CV), TF-IDF, and Keras Tokenizer (KT). Last, the proposed methodology was evaluated and compared to the stateof-the-art techniques, including K-Nearest Neighbors (KNN), Decision Tree (DT), Naive Bayes (NB), and Boosting SVM. The experimental results revealed that the proposed method in this research outperforms the existing technique as evidenced by the accuracy, precision, recall, and F1-score metrics, which respectively reached 71.5655%, 71.5483%, 71.5655%, and 71.0041%.","PeriodicalId":499081,"journal":{"name":"Jurnal Buana Informatika","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136054797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-01DOI: 10.24002/jbi.v14i02.7446
Luis Alpianto, Aditiya Hermawan, None Junaedi
Stocks as investment instruments in the capital market can provide benefits in capital gains but also have the risk of capital loss. Analysis and forecasting methods are needed to support investors. To achieve this, historical data and moving averages are used to reduce short-term random fluctuations in stock prices, and a linear regression algorithm to obtain accurate results by reducing the error rate and Mean Squared Error (MSE) value. The evaluation results show good accuracy with a strong correlation and a low Mean Absolute Percent Error (MAPE) value. In addition, testing on historical data is carried out to test the model and generate significant profits based on predictions from the model. According to the findings derived from the assessment, predicting stocks using the moving average and linear regression methods can help investors gain profits and reduce risk.
{"title":"Moving Average untuk Prediksi Harga Saham dengan Linear Regression","authors":"Luis Alpianto, Aditiya Hermawan, None Junaedi","doi":"10.24002/jbi.v14i02.7446","DOIUrl":"https://doi.org/10.24002/jbi.v14i02.7446","url":null,"abstract":"Stocks as investment instruments in the capital market can provide benefits in capital gains but also have the risk of capital loss. Analysis and forecasting methods are needed to support investors. To achieve this, historical data and moving averages are used to reduce short-term random fluctuations in stock prices, and a linear regression algorithm to obtain accurate results by reducing the error rate and Mean Squared Error (MSE) value. The evaluation results show good accuracy with a strong correlation and a low Mean Absolute Percent Error (MAPE) value. In addition, testing on historical data is carried out to test the model and generate significant profits based on predictions from the model. According to the findings derived from the assessment, predicting stocks using the moving average and linear regression methods can help investors gain profits and reduce risk.","PeriodicalId":499081,"journal":{"name":"Jurnal Buana Informatika","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135761882","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Dengan ini dilakukannya manajemen risiko menggunakan metode NIST SP 800-30 Revisi 1 dengan tujuan untuk melakukan penilaian risiko atas pengelolaan SIMBADA dan memberikan rekomendasi mitigasi berdasarkan Kontrol ISO/IEC 27001:2013 sehingga dapat menjadi acuan untuk minimalisir risiko yang mungkin terjadi. Hasilnya SIMBADA memiliki 20 daftar risiko yang berada pada level sangat tinggi, tinggi, sedang, dan rendah yang akan diberikan rekomendasi kontrol untuk penerapan keamanan sistem informasi. Terdapat 20 daftar ancaman risiko dan 54 rekomendasi mitigasi yang mengacu pada Kontrol ISO/IEC 27001:2013 dalam 11 klausul, 21 kontrol objektif, dan 39 kontrol keamanan yang dapat digunakan.Kata Kunci: manajemen risiko; keamanan informasi; NIST SP 800-30 Revisi 1; Kontrol ISO/IEC 27001:2013.
{"title":"Manajemen Risiko Keamanan Simbada Menggunakan Metode NIST SP 800-30 Revisi 1 dan Kontrol ISO/IEC 27001:2013","authors":"None Sindi Aprianti, None Renny Puspita Sari, None Ibnur Rusi","doi":"10.24002/jbi.v14i01.7043","DOIUrl":"https://doi.org/10.24002/jbi.v14i01.7043","url":null,"abstract":"Dengan ini dilakukannya manajemen risiko menggunakan metode NIST SP 800-30 Revisi 1 dengan tujuan untuk melakukan penilaian risiko atas pengelolaan SIMBADA dan memberikan rekomendasi mitigasi berdasarkan Kontrol ISO/IEC 27001:2013 sehingga dapat menjadi acuan untuk minimalisir risiko yang mungkin terjadi. Hasilnya SIMBADA memiliki 20 daftar risiko yang berada pada level sangat tinggi, tinggi, sedang, dan rendah yang akan diberikan rekomendasi kontrol untuk penerapan keamanan sistem informasi. Terdapat 20 daftar ancaman risiko dan 54 rekomendasi mitigasi yang mengacu pada Kontrol ISO/IEC 27001:2013 dalam 11 klausul, 21 kontrol objektif, dan 39 kontrol keamanan yang dapat digunakan.Kata Kunci: manajemen risiko; keamanan informasi; NIST SP 800-30 Revisi 1; Kontrol ISO/IEC 27001:2013.","PeriodicalId":499081,"journal":{"name":"Jurnal Buana Informatika","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135772920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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