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All Journal Jurnal INSYPRO (Information System and Processing) ESTIMASI: Journal of Statistics and Its Application Journal of Health, Education, Economics, Science, and Technology (J-HEST)
Arisona, Dian Christien
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Health Professions Library & Information Science Mathematics Other

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Klasifikasi Pesan Biasa, Operator, Spam, dan Debt Collector Menggunakan K-Nearest Neighbor.docx: Indonesia Arisona, Dian Christien; Adhi Wibowo, Gusti Ngurah; Siswanto, Siswanto; Gunawan, Gunawan
Jurnal INSYPRO (Information System and Processing) Vol 8 No 2 (2023)
Publisher : Prodi Sistem Informasi UIN Alauddin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/insypro.v8i2.41264

Abstract

This research implements a K-Nearest Neighbor (KNN) classifier to predict messages that recognized as ordinary messages, operator messages, spam messages, and debt collector messages. KNN is one of the classification algorithms that can be used to do a text classification, the prediction is made with consideration of distances between the object of observations. The data is messages that have been collected from SMS, whatsapp, and email therefore preprocessing using casefolding, stemming, tokenizing, and stopwords is necessary to do a modelling using KNN methods. The results of this research showed that the accuracy achieved from the training set was 93% and if we just focus on the messages that clasify as messages from debt collector then recall score from the testing set was 83%. This research is expected for further improvement and can be applied to recognizing messages from debt collector so that the victim can feel more comfortable.
Analisis Sentimen Persepsi Publik Tentang Program Merdeka Belajar Kampus Merdeka di X Mengggunakan Support Vector Machine Ardan, Dion Andrawan; Mukhsar, Mukhsar; Wibawa, Gusti Ngurah Adhi; Abapihi, Bahriddin; Arisona, Dian Christien; Tenriawaru, Andi
Journal of Health, Education, Economics, Science, and Technology (J-HEST) Vol. 6 No. 2 (2024): Journal of Health, Education, Economics, Science, and Technology
Publisher : Journal of Health, Education, Economics, Science, and Technology (J-HEST)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (282.693 KB) | DOI: 10.36339/

Abstract

Analisis sentimen adalah metode analisis data teks yang digunakan untuk mengklasifikasikan komentar ke dalam tiga kategori sentimen, yaitu positif, negatif, dan netral. Dalam penelitian ini, dilakukan pengklasifikasian sentimen menggunakan algoritma Support Vector Machine dengan teknik ekstraksi fitur TF-IDF. Tujuan dari penelitian ini adalah untuk mendapatkan gambaran tentang persepsi publik terhadap program Merdeka Belajar Kampus Merdeka melalui analisis komentar pengguna media sosial X. Penelitian ini juga bertujuan untuk mengevaluasi keakuratan hasil sentimen yang diperoleh menggunakan algoritma Support Vector Machine serta untuk memperoleh informasi dari hasil analisis sentimen tersebut. Klasifikasi sentimen dibagi menjadi tiga kategori, yaitu sentimen positif, negatif, dan netral. Hasil klasifikasi sentimen menunjukkan bahwa terdapat 287 komentar bersentimen netral, 242 komentar bersentimen positif, dan 91 komentar bersentimen negatif. Model klasifikasi dengan menggunakan kernel linear memiliki akurasi sebesar 82.25%, presisi sebesar 79.12%, dan recall sebesar 77.70%. Selain itu, pemodelan topik pada kelas sentimen negatif menghasilkan akurasi sebesar 80.79%, presisi sebesar 78.76%, dan recall sebesar 66.46% pada 10-fold cross validation.
Implementasi Model Long Short Term Memory (LSTM) Pada Proyeksi Harga Saham (Studi Kasus: PT. Pertamina Geothermal Energy (Persero)) Arisona, Dian Christien; Agusrawati, Agusrawati; Makkulau, Makkulau; Yahya, Irma; Wibawa, Gusti Ngurah Adhi; Baharuddin, Baharuddin; Fahyuni, Putri Riski
ESTIMASI: Journal of Statistics and Its Application Vol. 6, No. 2, Juli, 2025 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.v6i2.44963

Abstract

This research presents a comprehensive analysis of the Long Short Term Memory (LSTM) method in projecting the stock price of PT. Pertamina Geothermal Energy (Persero). Utilizing daily stock price data, the LSTM model achieves a high level of accuracy with a Mean Absolute Percentage Error (MAPE) value of 0.84%. The LSTM's gate mechanism (input, forget, output) enables it to store long-term information, controlling the flow of information to update memory, delete irrelevant data, and generate predictions. Optimized with backpropagation through time (BPTT) and activation functions, the LSTM model proves effective in investment decision making, providing valuable insights for investors and market players to anticipate stock price fluctuations. This research demonstrates the great potential of machine learning in financial analysis, particularly in stock price projection and time series analysis. The results indicate that LSTM can be a valuable tool for investors and financial analysts, enhancing their ability to make informed decisions.
Co-Authors Adhi Wibowo, Gusti Ngurah Agusrawati, Agusrawati Andi Tenriawaru Ardan, Dion Andrawan Baharuddin Baharuddin Bahriddin Abapihi Fahyuni, Putri Riski Gunawan Gunawan Irma Yahya Makkulau Makkulau Mukhsar . S Siswanto Wibawa, Gusti Ngurah Adhi