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All Journal International Journal of Electrical and Computer Engineering IJCCS (Indonesian Journal of Computing and Cybernetics Systems) JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Jurnal Teknologi Informasi dan Ilmu Komputer MUST: Journal of Mathematics Education, Science and Technology Jurnal Aplikasi Statistika & Komputasi Statistik Building of Informatics, Technology and Science Jurnal Informatika dan Sistem Informasi (JUISI) Teknika Jurnal Sistem Informasi Triguna Dharma (JURSI TGD)
Andreas, Christopher
Universitas Ciputra, Surabaya
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Mathematics

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Peramalan Inflasi dan Harga Minyak Mentah dengan Pendekatan Hybrid Statistika-Machine Learning dan Statistika-Deep Learning Christopher Andreas; Elizabeth Nathania Witanto; Yohana Jocelyn Guntur; Felicia Joshlyn Purnomo
Jurnal Sistem Informasi Triguna Dharma (JURSI TGD) Vol. 5 No. 1 (2026): EDISI JANUARI 2026
Publisher : STMIK Triguna Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53513/jursi.v5i1.12342

Abstract

Inflasi dan harga minyak mentah merupakan dua indikator ekonomi strategis yang memengaruhi stabilitas ekonomi nasional dan arah kebijakan publik. Peramalan yang akurat terhadap kedua variabel ini sangat penting untuk mendukung perencanaan fiskal, moneter, serta strategi sektor industri dan perdagangan. Karakteristik keduanya berbeda, dimana inflasi cenderung memiliki pola tren dan musiman yang relatif stabil, sedangkan harga minyak mentah bersifat fluktuatif dengan pengaruh faktor eksternal global. Perbedaan ini menuntut metode peramalan yang adaptif dan mampu bekerja baik pada kondisi data yang berbeda. Penelitian ini memiliki keterkaitan dengan Sustainable Development Goals (SDG 8 dan SDG 9). Penelitian ini bertujuan mengembangkan metode time series forecasting berbasis pendekatan hybrid melalui model statistika-machine learning dan statistika-deep learning. Pendekatan statistika dengan model Autoregressive Integrated Moving Average (ARIMA) digunakan untuk menangkap pola linear, kemudian hasil prediksi atau residual dari model ARIMA diproses lebih lanjut menggunakan algoritma machine learning yaitu Support Vector Regression (SVR) dan model deep learning yaitu Long Short-Term Memory (LSTM) untuk mempelajari pola non-linear. Dalam hal ini, evaluasi akurasi model diukur dengan metrik symmetric Mean Absolute Percentage Error (sMAPE). Hasil penelitian menunjukkan bahwa model ARIMA-SVR memiliki akurasi lebih baik dalam meramalkan data inflasi dengan nilai sMAPE sebesar 0,2072. Sebaliknya, model ARIMA-LSTM lebih akurat dalam meramalkan data harga minyak dengan nilai sMAPE sebesar 0,0548. Dengan demikian, pendekatan hybrid statistika-machine learning dan statistika-deep learning memiliki akurasi yang baik dalam memprediksi data yang bersifat time series.
Ontix: Blockchain-Based NFT Decentralized e-Ticketing Development Elizabeth Nathania Witanto; Christopher Andreas; Rudi Limantara; Louis Fernando; Lie Samuel Miracle Kristanto; Richie Reuben Hermanto
Teknika Vol. 15 No. 1 (2026): March 2026
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v15i1.1432

Abstract

Event ticketing systems, such as concerts, festivals, and sports matches, face persistent challenges, including ticket forgery, duplication, resale manipulation, and fraud in secondary markets. Centralized electronic ticketing systems, while digitized, remain vulnerable to identity theft, seller unaccountability, and unfair distribution due to their reliance on intermediaries and a single point of failure. To address these issues, this research introduces Ontix, a decentralized blockchain-based e-ticketing platform utilizing Non-Fungible Tokens (NFTs) compliant with the ERC-721 standard. By leveraging blockchain’s immutability, transparency, and decentralization, Ontix ensures verifiable ownership, tamper-proof ticket issuance, and automated transactions through smart contracts. The system enforces anti-scalping measures, including resale time and price limits, while enabling real-time QR-based validation directly linked to smart contracts. Ontix integrates Layer-2 Optimism Sepolia for scalability and lower gas fees, and employs the InterPlanetary File System (IPFS) via Pinata for decentralized metadata storage, alongside Cloudinary for media management. This hybrid architecture guarantees transparency, security, and operational efficiency. By eliminating intermediaries and automating ticket lifecycle management, Ontix provides an accountable, tamper-resistant, and low-cost e-ticketing ecosystem, as well as a user-centric ticketing ecosystem, representing a significant advancement toward the future of decentralized event management.
Machine Learning Approaches for Predicting Seasonal Stock Trends Gunawan, Jason Miracle; Andreas, Christopher; Saputri, Theresia Ratih Dewi
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 19, No 4 (2025): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.112504

Abstract

The financial market is vital for economic growth yet it often experiences volatility, particularly in Indonesia’s transportation sector where stock prices are strongly affected by seasonal fluctuations. Conventional forecasting methods often neglect these recurring patterns, lowering predictive accuracy. This study assesses the capability of Machine Learning algorithms to capture seasonality in stock price prediction, using PT Garuda Indonesia (Persero) Tbk (GIAA.JK)’s monthly data from August 2019 to May 2025, retrieved from Yahoo Finance. Four models–Linear Regression, Extreme Gradient Boosting (XGBoost), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM)–were trained and tested, with performance evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). Hyperparameter tuning was applied to XGBoost, LSTM, and GRU, while statistical validation employed the Kruskal-Wallis test. Results showed that the tuned GRU outperformed other models, achieving MAE of 5.90, RMSE of 7.33, and MAPE of 9.67%, demonstrating ‘excellent’ accuracy in modelling both short-term and seasonal dynamics. These findings highlight the superiority of GRU in modelling both short-term fluctuations and long-term seasonal dependencies in stock prices. The results contribute practical insights for investors and emphasize the importance of integrating seasonality in predictive models for volatile sectors
Penerapan Regresi Logistik, K-NN, dan Naïve Bayes Berbasis Pendekatan CRISP-DM dalam Memprediksi Penyakit Jantung Rayna Shera Chang; Natalie Grace Widjaja Kuswanto; Jessica Laurentia Tedja; Christopher Andreas
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.8518

Abstract

Heart disease remains the leading cause of mortality globally, despite having significant potential to be controlled through early detection and effective risk-factor management. To improve the accuracy and efficiency of early detection, machine learning technology is employed to develop predictive models for heart disease risk. The research aims to compare the performance of three classification algorithms in predicting heart disease risk to identify the most optimal model. This research applies the CRISP-DM methodology to build and compare predictive models for heart disease risk using three supervised learning algorithms: K-Nearest Neighbors (K-NN), Naïve Bayes, and Logistic Regression. The dataset used is a heart disease dataset obtained from the Kaggle platform, consisting of 10,000 records with variables such as Age, Blood Pressure, Smoking, Diabetes, Cholesterol, Triglyceride Level, Fasting Blood Sugar, and CRP Level. For the K-NN model, experiments were conducted using three values of k (k = 5, k = 10, and k = 20) to examine the effect of the number of neighbors on model performance. Meanwhile, the Naïve Bayes and Logistic Regression models were implemented using default parameters without additional tuning to ensure a consistent performance comparison. Model performance was evaluated using Accuracy and F1-Score metrics. The evaluation results indicate that the K-NN model with k = 5 achieved the best performance, with an accuracy of 0.7203 and an F1-Score of 0.7598, outperforming the Naïve Bayes and Logistic Regression models.
Studi Komparatif Model Time Series Untuk Peramalan Suhu Dan Analisis Korelasi Faktor Iklim Menggunakan Metodologi CRISP-DM Gunawan, Angela Melia; Tan, Sharon; Andreas, Christopher
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 13 No 4: Agustus 2026
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.134

Abstract

Perubahan iklim global telah memicu kebutuhan akan metode peramalan suhu yang lebih akurat dan adaptif. Penelitian ini bertujuan untuk mengevaluasi dan membandingkan performa tiga model peramalan dalam memprediksi suhu rata-rata bulanan di Kota Delhi, India. Penelitian ini menggunakan pendekatan time series forecasting berbasis statistik (SARIMA dan Holt-Winters Additive) serta machine learning (Random Forest). Selain itu, dilakukan analisis korelasi menggunakan metode Pearson Correlation untuk mengukur hubungan linier antara suhu dengan variabel iklim lain seperti kelembapan, kecepatan angin, dan tekanan udara. Penelitian ini mengadopsi metodologi CRISP-DM yang mencakup tahap business understanding, data understanding, data preparation, modeling, dan evaluation. Dataset yang digunakan bersumber dari Kaggle dan mencakup data harian dari tahun 2013 hingga 2017 yang diolah menjadi data bulanan. Hasil evaluasi menunjukkan bahwa model SARIMA memiliki performa terbaik dengan nilai MSE sebesar 1.613 dan MAPE sebesar 3.73%, yang tergolong prediksi akurasi tinggi. Analisis korelasi Pearson menunjukkan bahwa kelembapan dan tekanan udara memiliki korelasi negatif terhadap suhu rata-rata, sedangkan kecepatan angin menunjukkan korelasi positif, dan seluruh hubungan tersebut signifikan secara statistik. Hasil ini mengindikasikan bahwa variasi suhu tidak berdiri sendiri, melainkan dipengaruhi oleh interaksi kompleks antar faktor iklim. Temuan ini menegaskan pentingnya mempertimbangkan variabel pendukung dalam pengembangan model peramalan suhu agar hasil prediksi menjadi lebih komprehensif. Secara keseluruhan, hasil penelitian ini berkontribusi pada pemilihan model peramalan yang optimal untuk wilayah tropis dan mendukung pengambilan keputusan berbasis data dalam mitigasi perubahan iklim dan perencanaan kota.   Abstract Global climate change has triggered the need for more accurate and adaptive temperature forecasting methods. This study aims to evaluate and compare the performance of three forecasting models in predicting the monthly average temperature in Delhi City, India. This study uses a statistical time series forecasting approach (SARIMA and Holt-Winters Additive) and machine learning (Random Forest). In addition, a correlation analysis was performed using the Pearson Correlation method to measure the linear relationship between temperature and other climate variables such as humidity, wind speed, and air pressure. This research adopts the CRISP-DM methodology which includes the stages of business understanding, data understanding, data preparation, modeling, and evaluation. The dataset used is sourced from Kaggle and includes daily data from 2013 to 2017 which is processed into monthly data. The evaluation results show that the SARIMA model has the best performance with an MSE value of 1.613 and a MAPE of 3.73%, which is classified as a high accurate prediction. Pearson's correlation analysis shows that humidity and air pressure have a negative correlation with temperature, while wind speed shows a positive correlation, and all of these relationships are statistically significant. These results indicate that temperature variations do not occur independently, but are influenced by complex interactions between climate factors. These findings emphasize the importance of considering supporting variables in the development of temperature forecasting models in order to produce more comprehensive predictions. Overall, the results of this study contribute to the selection of optimal forecasting models for tropical regions and support data-driven decision-making in climate change mitigation and urban planning.
Co-Authors Ana, Elly Anifatul Faricha Antonio Nikolas Manuel Bonar Simamora Ayuning Dwis Cahyasari Dhyana Venosia Elfhira Juli Safitri Elizabeth Nathania Witanto Elly Pusporani Feevrinna Yohannes Harianto Felicia Joshlyn Purnomo Gunawan, Angela Melia Gunawan, Jason Miracle Halim Budi Hawwin Mardhiana Helda Urbhani Rosa Horidah Horidah Ilma Amira Rahmayanti Jessica Laurentia Tedja Lie Samuel Miracle Kristanto Louis Fernando M. Fariz Fadillah Mardianto Muhammad Achirul Nanda N Harianto Kristanto Natalie Grace Widjaja Kuswanto Naufal Ramadhan Al Akhwal Siregar Nur Chamidah Rayna Shera Chang Richie Reuben Hermanto Rika Susanti Rizza Sulistiana Rudi Limantara Sakinah Priandi Sediono, Sediono Siti Maghfirotul Ulyah Siti Maghfrotul Ulyah Suliyanto Tan, Sharon Theresia Ratih Dewi Saputri Toha Saifudin Yohana Jocelyn Guntur