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All Journal Jurnal Teknologi Informatika dan Komputer International Research on Big-data and Computer Technology (IRobot) Journal Computer Science and Informatic Systems : J-Cosys International Journal Software Engineering and Computer Science (IJSECS) Ilmu Komputer untuk Masyarakat SIENNA Jurnal TAM (Technology Acceptance Model)
Perdana, Andreas
Universitas Dharma Wacana
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Other

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Prediksi Kebutuhan Hidup Minimum/Layak Menggunakan Metode Autogresive Integrated Moving Average (ARIMA) Marcelli, Vanessa; Perdana, Andreas
Jurnal Teknologi Informatika dan Komputer Vol. 9 No. 2 (2023): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v9i2.1741

Abstract

Badan Pusat Statistik (BPS) Provinsi Lampung berperan dalam penentuan Standar Kebutuhan Hidup Minimum/Layak (KHL) sebagai tolok ukur dan untuk menegakkan tanggung jawab pekerjaan/birokrasi. Penentuan standar KHL tersebut sangat diperlukan dalam menentukan kebutuhan hidup minimum. Penentuan standar KHL harus mencakup ketentuan KHL dan komponen yang ada dalam aturan undang-undang. Dalam hal penyusunan komponen KHL, hal ini dilakukan selama 5 (lima) tahun dengan melakukan survei pasar setiap minggunya. Metode Autogresive Integrated Moving Average (ARIMA) menggunakan peramalan dengan teknik perekaman historis dari kumpulan data variabel di masa lalu, dengan mengidentifikasi pola deret waktu tersebut dapat memberikan ketepatan peramalan yang sangat akurat. Metode ARIMA memiliki tiga tahap: tahap identifikasi, tahap pengukuran dan pengujian, dan pemeriksaan diagnostik. Berdasarkan hasil penelitian ini dapat ditarik kesimpulan bahwa dalam peramalan jangka pendek, metode ARIMA sangat bisa diandalkan sebagaimana hasil uji metode penelitian ini yang menggunakan metode AR(1), MA(1), dan ARIMA(1.1.1). Dari ketiga model tersebut data yang diperoleh semuanya signifikan dan residualnya sudah white noise maka dapat disimpulkan bahwa modelnya dapat dikatakan baik. Pada tahun 2020 terdapat nilai peramalan tertinggi untuk Kebutuhan Hidup Minimum/Layak Provinsi Lampung yaitu senilai Rp. 2.005.646. Metode forecasting menggunakan ARIMA dapat menjadi salah satu metode peramalan Kebutuhan Hidup Minimum/Layak Provinsi Lampung, sehingga dapat membantu pemerintah Provinsi Lampung dalam menentukan kebijakan selanjutnya.
Analisis Perbandingan Metode Certainty Factor Dan Demster Shafer Pada Diagnosa Penyakit Mata Markus, Tamara Bertha; Ikhsanto, M. Nur; Perdana, Andreas
Journal Computer Science and Information Systems : J-Cosys Vol 3, No 2 (2023): J-Cosys - September 2023
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53514/jco.v3i2.419

Abstract

Katarak merupakan kekeruhan pada lensa mata yang berada didalam bola mata. Kekeruhan lensa atau katarak akan mengakibatkan cahaya terhalang untuk masuk kedalam mata sehingga daya penglihatan menjadi menurun. Penelitian ini sendiri bertujuan untuk mendiagnosa penyakit katarak dengan sistem pakar berdasarkan  metode Demster- Shafer dan metode Certainty-Factor pada Rumah Sakit Ahmad Yani Metro. sistem aplikasi diagnosa yang digunakan dalam pembuatan sistem pakar menggunakan metode agar dapat melakukan proses yang memperhitungkan hasil diagnosa pada sistem pakar. Metode yang digunakan pada diagnosa penyakit yaitu seperti  Demster-Shafer dan Certainty-Factor, pada penelitian ini metode tersebut dibandingkan secara kuantitatif yaitu dengan teori Confusion Matrix yang diambil dari hasil perhitungan dari kejadian gejala penyakit dan dinilai berdasarkan keyakinan dari pengetahuan pakar
Sentiment Analysis of Public Comments on YouTube Regarding the Inaugural Speech of the 8th President of Indonesia Using VADER and BERT Methods Alwi Ahmad Bastian; Andreas Perdana
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 1 (2025): APRIL 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i1.3472

Abstract

The research examines public reactions toward President Prabowo Subianto first presidential address in 2024 by studying YouTube comment sentiments. By utilizing sentiment analysis methods, this research combines two main approaches: The research combines VADER (Valence Aware Dictionary and Sentiment Reasoner) for initial sentiment labeling through predefined dictionary categories with BERT (Bidirectional Encoder Representations from Transformers) for more advanced classification. The dataset contains 10,306 comments which display a range of public opinions. Positive sentiment represents 4,943 comments which make up 49.26% of the total while neutral sentiment accounts for 4,336 comments at 43.21% and negative sentiment represents 756 comments at 7.53%. The BERT model reached an accuracy level of 97.01% which illustrates its capability to process contextual details and subtle data elements. VADER delivers rapid preliminary labeling results and BERT improves classification precision through its analysis of complex contexts. The study reveals how people perceive the new government while providing chances for creating public opinion monitoring techniques for social and political topics. Researchers, academics, and policymakers will find these findings valuable for comprehending public opinion dynamics during the digital age's continuous evolution
Forecasting Chili Prices in Metro City Using Long Short-Term Memory (LSTM) Gusti Made Gunadi; Andreas Perdana
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 1 (2025): APRIL 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i1.3526

Abstract

Cayenne pepper is one of the important commodities in the staple market in Indonesia which has a vital role in people's daily lives. Fluctuations in the price of cayenne pepper are often a challenge that impacts farmers and consumers, causing uncertainty in production and distribution planning. This research aims to develop a cayenne pepper price prediction model using the Long Short-Term Memory (LSTM) method, utilizing historical data from the data.metrokota.go.id portal for the period October 2023 to October 2024. By using LSTM, this model successfully captures long-term patterns in cayenne price data, with a Final Validation Loss of 0.00249 which indicates a high level of accuracy. The prediction results are expected to help farmers determine the optimal selling time, traders in managing stocks efficiently, and policy makers in formulating strategies to mitigate the impact of price fluctuations. In addition, this study highlights practical implications for stabilizing commodity markets, particularly in Metro City, as well as the relevance of these findings to be applied to other agricultural commodities.
Analisis Sentimen Masyarakat terhadap Kebijakan Penggunaan BBM Campuran Etanol di X (Twitter) Menggunakan Transformers (IndoBERT) Arraffy Abbyu Arrasyid; Andreas Perdana
Sienna Vol 7 No 1 (2026): Sienna Volume 7 Nomor 1 Juli 2026
Publisher : LPPM Universitas Muhammadiyah Kotabumi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47637/sienna.v7i1.2321

Abstract

Abstrak The transition toward sustainable energy has become a strategic priority for Indonesia, particularly through the implementation of bioethanol-blended fuel policies. However, public perception toward this policy remains diverse and dynamic, especially as expressed on social media platforms. This study aims to analyze public sentiment regarding the implementation of bioethanol-blended fuel (E10) policies on X (Twitter) and to compare the performance of traditional machine learning and Transformer-based models in sentiment classification. This research adopts a quantitative experimental approach using Natural Language Processing (NLP) techniques. A total of 2,501 tweets were collected through web crawling and processed using a Dual Pipeline Preprocessing approach. Sentiment labeling was conducted using the VADER method with manual validation. Two classification models were implemented, namely Support Vector Machine (SVM) as the baseline model and IndoBERT as the Transformer-based model. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. The results indicate that the IndoBERT model outperforms SVM, achieving an accuracy of 81.64% and an F1-score of 81.39%, compared to SVM with an accuracy of 72.46% and an F1-score of 72.02%. The performance improvement of 9.18% demonstrates the superiority of Transformer-based models in capturing contextual semantics in unstructured social media text. In addition, sentiment analysis results reveal that public opinion is predominantly positive toward the policy, although concerns regarding technical and economic aspects remain. This study contributes by providing empirical insights into public perception of energy policy and demonstrating the effectiveness of Transformer-based models for sentiment analysis in the Indonesian language context
Co-Authors ., Rusliyawati Adi Wibowo Alwi Ahmad Bastian Apri Candra Widyawati Apsiswanto, Untoro Ari Setiawan Arraffy Abbyu Arrasyid Budi Sutomo Budi Sutomo Doni andrian Dwi Hendriyanto Eriyani, Nurul Fitri, Aulia Alqusyah Gusti Made Gunadi Iin Marlina Ikhsanto, M Nur Ikhsanto, M. Nur Khoiriyah, Mafiqotul Kurnia Muludi Lia Aprita M Nur Ikhsanto Marcelli, Vanessa Markus, Tamara Bertha Muhammad Briliantino Muharni, Sita Nawawi, Ison Alvian Nofisa, Hendri Pusfitasari, Desi Putri, Regita Andini Ridwan Yusuf Saputri, Tri Aristy Sulistiyanto Sulistiyanto Vanessa Marcelli Wilda Yulianti Y. Suhendro