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All Journal Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Informatika: Jurnal Pengembangan IT
Dewi Pergiwati
Universitas Dian Nuswantoro
Computer Science & IT Economics, Econometrics & Finance Health Professions Industrial & Manufacturing Engineering Social Sciences

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Optimasi Investasi di Pasar Saham Indonesia: Meningkatkan Keputusan Investasi dengan Prediksi IHSG menggunakan Decision Tree Dwi Eko Waluyo; Cinantya Paramita; Hayu Wikan Kinasih; Fauzi Adi Rafrastara; Dewi Pergiwati
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 7, No 1 (2024): JANUARI 2024
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/ja.v7i1.1876

Abstract

Pasar saham Indonesia merupakan pilar ekonomi yang vital, memfasilitasi perolehan modal bagi perusahaan serta menawarkan peluang investasi bagi individu hingga korporasi besar. Keberhasilan investasi sangat dipengaruhi oleh kemampuan memahami faktor-faktor yang menentukan pergerakan harga saham. Teknologi dan analisis data, khususnya melalui algoritma Decision Tree, dapat membantu memprediksi pergerakan Indeks Harga Saham Gabungan (IHSG), sehingga mendukung keputusan investasi yang lebih baik. Pengabdian masyarakat bertajuk "Optimasi Investasi di Pasar Saham Indonesia" dirancang untuk meningkatkan literasi investasi di kalangan mahasiswa, pengusaha dan pemegang saham, melalui pengembangan system analisis berbasis Decision Tree untuk prediksi IHSG. Program ini mencakup penelitian awal, pengembangan dan validasi model prediksi, pelatihan dan edukasi, implementasi, serta evaluasi dan penyempurnaan berbasis MOS, dengan tujuan akhir meningkatkan keberhasilan investasi di pasar saham Indonesia, seraya mengintegrasikan pengetahuan di bidang komputer, AI, dan keuangan. Materi pelatihan mencakup dasar analisis teknikal dan fundamental, analisis Decision Tree, optimasi portofolio, dan strategi manajemen risiko, dilengkapi dengan alat machine learning. Evaluasi pasca pelatihan menggunakan metode Mean Opinion Score (MOS) menunjukkan tingkat kepuasan tinggi dengan skor 97.08% untuk Fungsionalitas, 96.09% untuk Keandalan, dan 98.09% untuk Kegunaan, menekankan efektivitas algoritma Decision Tree dalam memprediksi IHSG dan meningkatkan keputusan investasi.
Aplikasi Prediksi IHSG Berbasis Web Dengan Integrasi Multi-Algoritma Dwi Eko Waluyo; Cinantya Paramita; Hayu Wikan Kinasih; Dewi Pergiwati; Fauzi Adi Rafrastara
Jurnal Informatika: Jurnal Pengembangan IT Vol 9, No 2 (2024)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v9i2.6193

Abstract

The four regression algorithms used in predicting the Composite Stock Price Index (IHSG) have contributed significantly, as the test results show that the Decision Tree algorithm outperforms k-Nearest Neighbor, Linear Regression, and Random Forest, especially in terms of Mean Squared Error (MSE) and R2 score. The stages of data collection, pre-processing, and modeling, followed by model performance measurement, have provided valuable insights into the effectiveness of each algorithm. The success of the Decision Tree in this testing has further propelled its development into a web-based application. This conversion process, following the outlined flowchart, integrates various essential aspects of the model, including user interface and back-end integration, ensuring that the application can be accessed and used efficiently and effectively. Furthermore, the black box testing and User Acceptance Testing (UAT) results, using the Mean Opinion Score (MOS), enhance the validity and reliability of the application. Black box testing involving 2 features with 37 steps demonstrates the system's effectiveness in producing valid outputs, from the initial menu display to the prediction results. Additionally, UAT involving students and entrepreneurs as respondents provides in-depth insights into user acceptance. With a focus on functionality at 97.08%, reliability at 96.09%, and usability at 98.09%, UAT yields high scores in all three aspects, with usability achieving the highest score. These results not only confirm the efficiency of the system in performing its functions but also indicate a high level of user satisfaction, strongly suggesting the potential for widespread adoption of this application in the future.
Komparasi dan Implementasi Algoritma Regresi Machine Learning untuk Prediksi Indeks Harga Saham Gabungan Dwi Eko Waluyo; Hayu Wikan Kinasih; Cinantya Paramita; Dewi Pergiwati; Rajendra Nohan; Fauzi Adi Rafrastara
Jurnal Informatika: Jurnal Pengembangan IT Vol 9, No 1 (2024)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v9i1.6105

Abstract

Indeks Harga Saham Gabungan (IHSG) or Indonesia Composite Index (ICI) is part of the macro indicators of a country that describes the economic condition of a country. ICI is an interesting study to research since its existence will be able to show market sentiment regarding an event that occurred in a country. This research tries to predict the ICI in the future based on historical data. The dataset used in this research is publicly available in Yahoo Finance. The experiment is conducted by implementing some regression machine learning algorithms, such as Decision Tree, Random Forest, k-Nearest Neighbor (kNN), and Linear Regression. As a result, Decision Tree has the lowest MSE value compared to other methods: 1268.242. In this research, a website-based application prototype was also developed that can be used to view IHSG graphs and make future predictions, using the 4 (four) tested algorithms.
Co-Authors Cinantya Paramita Dwi Eko Waluyo Fauzi Adi Rafrastara Hayu Wikan Kinasih Rajendra Nohan