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All Journal Jurnal Informatika Jurnal Sistem Cerdas JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Informatika: Jurnal Pengembangan IT
Abyasa, Rayhan
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Automotive Engineering Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering

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Prediksi Volume Ekspor Udang Menggunakan Indeks Google Trend dan Faktor Berpengaruh Lainnya dengan Machine Learning Abyasa, Rayhan; Nurmawati, Erna
JUSTIN (Jurnal Sistem dan Teknologi Informasi) Vol 12, No 3 (2024)
Publisher : Jurusan Informatika Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/justin.v12i3.78900

Abstract

Udang merupakan salah satu komoditas ekspor unggulan Indonesia pada sektor perikanan yang berkontribusi sebesar 34,57% dari nilai ekspor perikanan pada tahun 2022. Indonesia juga masuk kedalam enam negara pembudidaya dan eksportir udang terbesar di dunia. Untuk memantau dan mengevaluasi target yang telah ditentukan oleh pemerintah, dibutuhkan model peramalan yang akurat. Untuk meningkatkan akurasi peramalan, indeks google trend, kurs rupiah, dan harga udang internasional ditambahkan sebagai variabel eksogen. Kata kunci indeks google trend yang digunakan diambil dari sisi eksportir dan importir seperti "ekspor udang" untuk sisi eksportir dan terjemahan kata "udang indonesia" untuk sisi importir. Penelitian ini menggunakan machine learning dengan model XGBoost dan LSTM. Model XGBoost menghasilkan nilai MAPE sebesar 10,08% sedangkan model LSTM menghasilkan nilai MAPE sebesar 12,40%. Penelitian ini menghasilkan kesimpulan bahwa model terbaik untuk volume ekspor udang Indonesia adalah model XGBoost berdasarkan nilai MAPE.
Web Service Integration: Data Exchange among Area Sampling Framework, Paddy Sampling, and CAPI Cropping Systems Finmansyah Akbar, Edo; Nurmawati, Erna; Abyasa, Rayhan
Jurnal Sistem Cerdas Vol. 7 No. 3 (2024)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v7i3.411

Abstract

Statistics Indonesia (BPS) is responsible for providing agriculture data. BPS collects statistics on paddy paddy production by performing a survey that involves sampling paddy plots using the Area Sampling Framework (ASF). The ASF survey is conducted monthly. The ASF System receives information from the Paddy Commodity Cropping Sampling System to prepare the sample frame and withdraw samples. This is done by the Sub-Directorate of Sample Frame Development (PKS Sub-Directorate). The existing system requires human processing of ASF results to modify the paddy observation code. This processing is carried out by the Sub-Directorate of Food Crops and the data is prepared by the Sub-Directorate of Sample Frame Development (PKS) before being uploaded into the Paddy Commodity Cropping Sampling System. The findings of sample retrieval by the Paddy Commodity Cropping Sampling System will be transmitted to the Sub-Directorate of Data Processing Integration (Sub-Directorate of IPD) and thereafter uploaded into the CAPI System for Paddy Cropping. The PKS Sub Directorate has identified many processes in the existing system that are deemed to be less efficient. The current inefficiency of the business process is caused by the manual execution of various tasks in the ASF system, such as sending data via email, modifying the paddy observation code, and sending the modified code results. Additionally, the data preparation process relies on additional applications, and sample documents from the Paddy Crop Sampling System are manually sent to the CAPI Cropping Sampling System. Hence, there is a requirement for enhancing the process flow of paddy harvesting sample. The lack of integration across systems necessitates manual execution of the process. This research proposes enhancing the Paddy Commodity Crop Sampling System by introducing new functionalities for modifying the paddy observation code and data preparation. Additionally, it suggests utilizing web services to integrate the ASF System, Paddy Commodity Crop Sampling System, and CAPI Cropping System
Predicting Stock Price Movements with Technical, Fundamental, and Sentiment Analysis Using the LSTM Model Saputra, Muhammad Ighfar; Nurmawati, Erna; Abyasa, Rayhan
Jurnal Informatika Vol 12, No 1 (2025): April
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/inf.v12i1.22299

Abstract

The challenge of minimizing risk and maximizing profit is what traders in the stock market have been endeavoring to solve for years. Stock prices typically exhibit the characteristic of volatility, influenced by various factors and necessitate a substantial amount of data to identify patterns in price movements. Considering the significant data requirements and the rapid advancement of big data and artificial intelligence, the LSTM (Long-Short Term Memory) model stands as a suitable approach for utilization in Deep Learning. The independent variables employed encompass technical indicator variables, currency exchange rates, interest rates, the Jakarta Composite Index (IHSG), and sentiment data extracted from Twitter tweets. The results indicate that sentiment analysis using the IndoBERT model achieved an accuracy of 0.69, while LSTM analysis produced the model with the smallest error for the fourth (4th) combination of variables, comprising closing price, technical indicators, IHSG, exchange rate, and Twitter sentiment, as well as the twelfth (12th) combination of variables, encompassing closing price, technical indicators, and IHSG. These combinations yielded average RMSE errors of 1.765E-04 and 1.978E-04, respectively. Following hyperparameter optimization, the best-identified model was the fourth (4th) combination of variables, yielding a minimal error of 7.580E-05 and an RMSE of 332.66 in the evaluation of test data. 
Optimalisasi Portofolio Saham Syariah Berbasis Prediksi Menggunakan Long Short-Term Memory (LSTM) Nurmawati, Erna; Abyasa, Rayhan; Putra, Raditya Amanta
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 2 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i2.8421

Abstract

Saham merupakan salah satu jenis investasi aset finansial yang berpotensi untuk memberikan tingkat imbal balik yang tinggi sehingga menjadi salah satu instrument investasi yang popular. Salah satu jenis saham yang popular di Indonesia adalah saham syariah yang didukung kuat dengan ajaran agama islam (shariah compliant). Saham syariah mempunyai kinerja yang baik jika dibandingkan dengan saham konvensional ketika terjadi krisis keuangan ditandai dengan risiko indeks yang lebih kecil. Investor saham selalu menginginkan hasil timbal balik yang maksimal dengan risiko seminimal mungkin. Keinginan tersebut dapat tercapai dengan menyeleksi saham dengan return terbesar lalu melakukan optimalisasi pada potofolio saham. Salah satu metode seleksi saham yang dapat dilakukan adalah dengan memprediksi harga saham dengan menggunakan model LSTM pada indeks JII. Saham dengan return terbesar sesuai dengan hasil prediksi akan dimasukkan ke dalam satu portofolio yang akan dioptimalisasi dengan metode Mean-Variance (MV) dan Equal Weight (EW) yang akan diambil metode terbaik. Sebagai pembanding, portofolio dengan saham yang dipilih secara acak akan dibentuk dan dibandingkan hasilnya. Hasil penelitian menunjukkan portofolio yang dibentuk dengan menggunakan prediksi model LSTM dan metode optimalisasi MV memiliki keseimbangan dalam nilai mean return bulanan, standar deviasi bulanan, sharpe ratio bulanan, serta simulasi investasi sepanjang tahun 2023.
Co-Authors Finmansyah Akbar, Edo Nurmawati, Erna Putra, Raditya Amanta Saputra, Muhammad Ighfar