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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Journal of Intelligent Decision Support System (IDSS) Jurnal Rekayasa Sistem Informasi dan Teknologi
Yudha Randa Mad’hika
Universitas Indraprasta PGRI Jakarta
Computer Science & IT Decision Sciences, Operations Research & Management

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Gold Price Prediction Using the ARIMA and LSTM Models Madhika, Yudha Randa; Kusrini, Kusrini; Hidayat, Tonny
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 3 (2023): Article Research Volume 7 Issue 3, July 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.12461

Abstract

For some investors who are interested in investing for the long term, gold is one of the promising options because the price of gold has recently continued to increase. In the current condition, gold investors generally use instinct and guesswork in investing in gold because there is a benchmark gold price based on world market prices. Many empirical studies identify factors that affect gold prices to forecast them. Factual and econometric analysis recommend different informative factors. This study investigates the influence of gold prices and five supporting variables in the form of economic indicators, namely crude oil price, federal funds effective rate, consumer price index, effective exchange rate and S&P 500 stock market index between 2002 and 2022. Models were built using ARIMA and LSTM methods, evaluated using Root Mean Square Error (RMSE) and Mean Absolute Percent Error (MAPE). With a dataset allocation of 80% for training data and 20% for testing data, the comparison of actual gold prices with the predicted values of each model shows that LSTM has the best performance compared to the ARIMA (0,1,1) model where the LSTM model has an RMSE value of 8.124 and a MAPE value of 0.023. The models also show that economic indicators affect the ounce price of gold.
Segmentation of Waste Management of All Provinces in Indonesia Using K-Means Clustering Mad'hika, Yudha Randa; Pirman, Arif
Journal of Intelligent Decision Support System (IDSS) Vol 8 No 2 (2025): June: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v8i2.296

Abstract

The amount of waste in Indonesia continues to increase along with the increasing population and welfare. Waste data there are so many waste data throughout Indonesia that it is difficult to determine which managed waste data from provinces will be taken so a recommendation is needed to determine it. Mapping waste management based on the results of waste managed into animal feed raw materials, compost raw materials, recycled raw materials, up-cycle raw materials and energy source raw materials is expected to help the government (or local government) make more appropriate policies. Therefore, this research uses a clustering method, namely k-means clustering. Based on the results of the analysis using the elbow method, the optimal number of clusters selected in this study is k=2. Next, the process of clustering managed waste is carried out using the K-Means clustering algorithm. The clustering results on waste management data display data information with a low level of proportion of waste management volume consisting of 28 provinces and a high level of proportion of waste management volume consisting of 6 provinces. Based on the evaluation of the k-means clustering results, the maximum value of the silhouette coefficient = 0.940 and the Davies-Bouldin index value = 0.430. The concrete recommendations are to make the province with the highest proportion of waste management as a pilot project for the construction of PLTSa, develop a Public-Private Partnership scheme for investment in waste-to-energy processing technology and accelerate licensing and local regulations that support the operationalization of WtE.
KAJIAN KONSEPTUAL INTEGRASI ARTIFICIAL INTELLIGENCE DALAM PEMBELAJARAN BERBASIS OUTCOME-BASED EDUCATION (OBE) Akhmad Aris Tantowi; Yudha Randa Mad’hika; Ahmad Yusuf Malik
Jurnal Rekayasa Sistem Informasi dan Teknologi Vol. 3 No. 4 (2026): Mei
Publisher : Yayasan Nuraini Ibrahim Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70248/jrsit.v3i4.3785

Abstract

Perkembangan Artificial Intelligence (AI) telah mendorong transformasi sistem pendidikan menuju pembelajaran yang lebih adaptif, personal, dan berbasis data. Dalam konteks Outcome-Based Education (OBE), integrasi AI menjadi penting karena mampu mendukung pencapaian capaian pembelajaran secara lebih efektif dan terukur. Penelitian ini bertujuan untuk mengkaji secara konseptual integrasi Artificial Intelligence dalam pembelajaran berbasis Outcome-Based Education (OBE) serta menganalisis manfaat, tantangan, dan implikasinya terhadap transformasi pendidikan digital di Indonesia. Penelitian menggunakan metode literature review dengan menganalisis 25 artikel ilmiah nasional dan internasional yang dipublikasikan pada tahun 2020–2026 dan diperoleh melalui Google Scholar serta GARUDA Kemdikbud. Seleksi literatur dilakukan berdasarkan relevansi topik, kualitas sumber, dan keterkaitan dengan implementasi AI dalam pendidikan berbasis OBE. Hasil kajian menunjukkan bahwa integrasi AI mampu meningkatkan personalisasi pembelajaran, efektivitas evaluasi capaian pembelajaran, efisiensi pengelolaan pembelajaran, serta penguatan keterampilan abad ke-21. Selain itu, AI mendukung adaptive learning, learning analytics, intelligent tutoring system, dan automatic assessment yang selaras dengan prinsip OBE. Namun demikian, implementasi AI masih menghadapi tantangan berupa keterbatasan infrastruktur teknologi, rendahnya literasi digital, kesiapan sumber daya manusia, serta persoalan etika dan keamanan data. Penelitian ini memberikan kontribusi konseptual dalam pengembangan model pembelajaran berbasis OBE yang terintegrasi dengan AI sebagai bagian dari transformasi pendidikan di era Society 5.0..
Co-Authors Ahmad Yusuf Malik Akhmad Aris Tantowi Kusrini Pirman, Arif TONNY HIDAYAT